Готові списки джерел за темами / Machine Learning, Artificial Intelligence, Regularization Methods

Добірка наукової літератури з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

Abidine, M’hamed Bilal, and Belkacem Fergani. "Activity recognition from smartphone data using weighted learning methods." Intelligenza Artificiale 15, no. 1 (July 28, 2021): 1–15. http://dx.doi.org/10.3233/ia-200059.

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Mobile phone based activity recognition uses data obtained from embedded sensors to infer user’s physical activities. The traditional approach for activity recognition employs machine learning algorithms to learn from collected labeled data and induce a model. To enhance the accuracy and hence to improve the overall efficiency of the system, the good classifiers can be combined together. Fusion can be done at the feature level and also at the decision level. In this work, we propose a new hybrid classification model Weighted SVM-KNN to perform automatic recognition of activities that combines a Weighted Support Vector Machines (WSVM) to learn a model with a Weighted K-Nearest Neighbors (WKNN), to classify and identify the ongoing activity. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our method outperforms the state-of-the-art on a large benchmark datasets.

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Fokkema, Marjolein, Dragos Iliescu, Samuel Greiff, and Matthias Ziegler. "Machine Learning and Prediction in Psychological Assessment." European Journal of Psychological Assessment 38, no. 3 (May 2022): 165–75. http://dx.doi.org/10.1027/1015-5759/a000714.

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Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.

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Кабанихин, С. И. "Inverse Problems and Artificial Intelligence." Успехи кибернетики / Russian Journal of Cybernetics, no. 3 (October 11, 2021): 33–43. http://dx.doi.org/10.51790/2712-9942-2021-2-3-5.

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В данной работе приведен анализ взаимосвязей теории обратных и некорректных задач и математических аспектов искусственного интеллекта. Показано, что при анализе вычислительных алгоритмов, которые условно можно отнести к вычислительному искусственному интеллекту (машинное обучение, природоподобные алгоритмы, методы анализа и обработки данных), возможно, а подчас и необходимо, использовать результаты и подходы, развитые в теории и численных методах решения обратных и некорректных задач, такие как регуляризация, условная устойчивость и сходимость, использование априорной информации, идентифицируемость, чувствительность, усвоение данных. This paper analyzes the relationship between the theory of inverse and incorrect problems and the mathematical aspects of artificial intelligence. It is shown that computational algorithms that can be categorized as computational artificial intelligence (machine learning, nature-like algorithms, data analysis and processing) can or should be analyzed with the approaches developed for the theory and numerical methods for solving inverse and incorrect problems. They are regularization, conditional stability and convergence, the use of a priori information, identifiability, sensitivity, data assimilation.

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Mohammad-Djafari, Ali. "Interaction between Model Based Signal and Image Processing, Machine Learning and Artificial Intelligence." Proceedings 33, no. 1 (November 28, 2019): 16. http://dx.doi.org/10.3390/proceedings2019033016.

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Signale and image processing has always been the main tools in many area and in particular in Medical and Biomedical applications. Nowadays, there are great number of toolboxes, general purpose and very specialized, in which classical techniques are implemented and can be used: all the transformation based methods (Fourier, Wavelets, ...) as well as model based and iterative regularization methods. Statistical methods have also shown their success in some area when parametric models are available. Bayesian inference based methods had great success, in particular, when the data are noisy, uncertain, incomplete (missing values) or with outliers and where there is a need to quantify uncertainties. In some applications, nowadays, we have more and more data. To use these “Big Data” to extract more knowledge, the Machine Learning and Artificial Intelligence tools have shown success and became mandatory. However, even if in many domains of Machine Learning such as classification and clustering these methods have shown success, their use in real scientific problems are limited. The main reasons are twofold: First, the users of these tools cannot explain the reasons when the are successful and when they are not. The second is that, in general, these tools can not quantify the remaining uncertainties. Model based and Bayesian inference approach have been very successful in linear inverse problems. However, adjusting the hyper parameters is complex and the cost of the computation is high. The Convolutional Neural Networks (CNN) and Deep Learning (DL) tools can be useful for pushing farther these limits. At the other side, the Model based methods can be helpful for the selection of the structure of CNN and DL which are crucial in ML success. In this work, I first provide an overview and then a survey of the aforementioned methods and explore the possible interactions between them.

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Dif, Nassima, and Zakaria Elberrichi. "Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 4 (October 2020): 62–81. http://dx.doi.org/10.4018/ijcini.2020100104.

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Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.

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Luo, Yong, Liancheng Yin, Wenchao Bai, and Keming Mao. "An Appraisal of Incremental Learning Methods." Entropy 22, no. 11 (October 22, 2020): 1190. http://dx.doi.org/10.3390/e22111190.

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As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data. It is expected to have the ability of memorization and it is regarded as one of the ultimate goals of artificial intelligence technology. However, incremental learning remains a long term challenge. Modern deep neural network models achieve outstanding performance on stationary data distributions with batch training. This restriction leads to catastrophic forgetting for incremental learning scenarios since the distribution of incoming data is unknown and has a highly different probability from the old data. Therefore, a model must be both plastic to acquire new knowledge and stable to consolidate existing knowledge. This review aims to draw a systematic review of the state of the art of incremental learning methods. Published reports are selected from Web of Science, IEEEXplore, and DBLP databases up to May 2020. Each paper is reviewed according to the types: architectural strategy, regularization strategy and rehearsal and pseudo-rehearsal strategy. We compare and discuss different methods. Moreover, the development trend and research focus are given. It is concluded that incremental learning is still a hot research area and will be for a long period. More attention should be paid to the exploration of both biological systems and computational models.

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Alcin, Omer F., Abdulkadir Sengur, Jiang Qian, and Melih C. Ince. "OMP-ELM: Orthogonal Matching Pursuit-Based Extreme Learning Machine for Regression." Journal of Intelligent Systems 24, no. 1 (March 1, 2015): 135–43. http://dx.doi.org/10.1515/jisys-2014-0095.

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AbstractExtreme learning machine (ELM) is a recent scheme for single hidden layer feed forward networks (SLFNs). It has attracted much interest in the machine intelligence and pattern recognition fields with numerous real-world applications. The ELM structure has several advantages, such as its adaptability to various problems with a rapid learning rate and low computational cost. However, it has shortcomings in the following aspects. First, it suffers from the irrelevant variables in the input data set. Second, choosing the optimal number of neurons in the hidden layer is not well defined. In case the hidden nodes are greater than the training data, the ELM may encounter the singularity problem, and its solution may become unstable. To overcome these limitations, several methods have been proposed within the regularization framework. In this article, we considered a greedy method for sparse approximation of the output weight vector of the ELM network. More specifically, the orthogonal matching pursuit (OMP) algorithm is embedded to the ELM. This new technique is named OMP-ELM. OMP-ELM has several advantages over regularized ELM methods, such as lower complexity and immunity to the singularity problem. Experimental works on nine commonly used regression problems indicate that the investigated OMP-ELM method confirms these advantages. Moreover, OMP-ELM is compared with the ELM method, the regularized ELM scheme, and artificial neural networks.

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Homayouni, Haleh, and Eghbal G. Mansoori. "Manifold regularization ensemble clustering with many objectives using unsupervised extreme learning machines." Intelligent Data Analysis 25, no. 4 (July 9, 2021): 847–62. http://dx.doi.org/10.3233/ida-205362.

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Spectral clustering has been an effective clustering method, in last decades, because it can get an optimal solution without any assumptions on data’s structure. The basic key in spectral clustering is its similarity matrix. Despite many empirical successes in similarity matrix construction, almost all previous methods suffer from handling just one objective. To address the multi-objective ensemble clustering, we introduce a new ensemble manifold regularization (MR) method based on stacking framework. In our Manifold Regularization Ensemble Clustering (MREC) method, several objective functions are considered simultaneously, as a robust method for constructing the similarity matrix. Using it, the unsupervised extreme learning machine (UELM) is employed to find the generalized eigenvectors to embed the data in low-dimensional space. These eigenvectors are then used as the base point in spectral clustering to find the best partitioning of the data. The aims of this paper are to find robust partitioning that satisfy multiple objectives, handling noisy data, keeping diversity-based goals, and dimension reduction. Experiments on some real-world datasets besides to three benchmark protein datasets demonstrate the superiority of MREC over some state-of-the-art single and ensemble methods.

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Nayef, Bahera Hani, Siti Norul Huda Sheikh Abdullah, Rossilawati Sulaiman, and Zaid Abdi Al Kareem Alyasseri. "VARIANTS OF NEURAL NETWORKS: A REVIEW." Malaysian Journal of Computer Science 35, no. 2 (April 29, 2022): 158–78. http://dx.doi.org/10.22452/mjcs.vol35no2.5.

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Machine learning (ML) techniques are part of artificial intelligence. ML involves imitating human behavior in solving different problems, such as object detection, text handwriting recognition, and image classification. Several techniques can be used in machine learning, such as Neural Networks (NN). The expansion in information technology enables researchers to collect large amounts of various data types. The challenging issue is to uncover neural network parameters suitable for object detection problems. Therefore, this paper presents a literature review of the latest proposed and developed components in neural network techniques to cope with different sizes and data types. A brief discussion is also introduced to demonstrate the different types of neural network parameters, such as activation functions, loss functions, and regularization methods. Moreover, this paper also uncovers parameter optimization methods and hyperparameters of the model, such as weight, the learning rate, and the number of iterations. From the literature, it is notable that choosing the activation function, loss function, number of neural network layers, and data size is the major factor affecting NN performance. Additionally, utilizing deep learning NN resulted in a significant improvement in model performance for a variety of issues, which became the researcher's attention.

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Cai, Yingfeng, Youguo He, Hai Wang, Xiaoqiang Sun, Long Chen, and Haobin Jiang. "Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model." International Journal of Advanced Robotic Systems 14, no. 1 (February 14, 2016): 172988141769231. http://dx.doi.org/10.1177/1729881417692311.

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The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

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Дисертації з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

ROSSI, ALESSANDRO. "Regularization and Learning in the temporal domain." Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1006818.

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The main proposals of this thesis concern the formulation of a new approach to classic Machine Learning optimization procedures, so as to inspire some insights about the recent rush to gold in most of the Artificial Intelligence applications. Crucial aspects we would like to raise up can be associated to the current thirst of data that characterizes popular approaches, above all in Deep Learning. The high representational power of these kind of structures allows us to achieve state of the art results in most of the existent Machine Learning benchmarks, provided that a lot of labeled data are available in order to tune the large number of learnable parameters. All the information about the analyzed phenomena is assumed to be available at the beginning of the training and, usually, provided to the agent in a shuffled order to improve the final result. However, when looking to the fundamental ambition of Artificial Intelligence to simulate human behavior, it is straightforward to note that these problems have been embedded in a framework which is completely different from the biological environment. Our formulation, inspired by natural behaviors in biology, is related to general Regularization Methods for Machine Learning. As for other natural phenomena, we tried to provide a model described by Laws of Physics, taking into account the existent forces and the final goal of the problem. This approach allows us to recreate, analogously to the studies on systems of particles considered in Classical Mechanics, laws of motion that implement a regularization effect based on the temporal smoothness of the environment. The results came up to be a generalization of classical algorithms for discrete optimization, empirically reinforcing the soundness of the theory. The main contributions of the thesis regard an extensive experimental analysis necessary to validate the proposed model and analyze the effects of its hyper-parameters. Furthermore, we propose a novel learning structure to extend classic semi-supervised learning techniques to on-line learning and include general kind of constraints.

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Lu, Yibiao. "Statistical methods with application to machine learning and artificial intelligence." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44730.

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This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization in the context of supervised learning in order to achieve both nice theoretical properties (like thin-plate splines) and good performance over complex region (like soap film smoother). In Chapter 2, we propose an innovative static path-planning algorithm called m-A* within an environment full of obstacles. Theoretically we show that m-A* reduces the number of vertex. In the simulation study, our approach outperforms A* armed with standard L1 heuristic and stronger ones such as True-Distance heuristics (TDH), yielding faster query time, adequate usage of memory and reasonable preprocessing time. Chapter 3 proposes m-LPA* algorithm which extends the m-A* algorithm in the context of dynamic path-planning and achieves better performance compared to the benchmark: lifelong planning A* (LPA*) in terms of robustness and worst-case computational complexity. Employing the same beamlet graphical structure as m-A*, m-LPA* encodes the information of the environment in a hierarchical, multiscale fashion, and therefore it produces a more robust dynamic path-planning algorithm. Chapter 4 focuses on an approach for the prediction of spot electricity spikes via a combination of boosting and wavelet analysis. Extensive numerical experiments show that our approach improved the prediction accuracy compared to those results of support vector machine, thanks to the fact that the gradient boosting trees method inherits the good properties of decision trees such as robustness to the irrelevant covariates, fast computational capability and good interpretation.

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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.

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Le, Truc Duc. "Machine Learning Methods for 3D Object Classification and Segmentation." Thesis, University of Missouri - Columbia, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877153.

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Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation.

The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset.

The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation.

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Michael, Christoph Cornelius. "General methods for analyzing machine learning sample complexity." W&M ScholarWorks, 1994. https://scholarworks.wm.edu/etd/1539623860.

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During the past decade, there has been a resurgence of interest in applying mathematical methods to problems in artificial intelligence. Much work has been done in the field of machine learning, but it is not always clear how the results of this research should be applied to practical problems. Our aim is to help bridge the gap between theory and practice by addressing the question: "If we are given a machine learning algorithm, how should we go about formally analyzing it?" as opposed to the usual question: "how do we write a learning algorithm we can analyze?".;We will consider algorithms that accept randomly drawn training data as input, and produce classification rules as their outputs. For the most part our analyses will be based on the syntactic structure of these classification rules; for example, if we know that the algorithm we want to analyze will only output logical expressions that are conjunctions of variables, we can use this fact to facilitate our analysis.;We use a probabilistic framework for machine learning, often called the pac model. In this framework, one asks whether or not a machine learning algorithm has a high probability of generating classification rules that "usually" make the right classification (pac means probably approximately correct). Research in the pac framework can be divided into two subfields. The first field is concerned with the amount of training data that is needed for successful learning to take place (success being defined in terms of generalization ability); the second field is concerned with the computational complexity of learning once the training data have been selected. Since most existing algorithms use heuristics to deal with the problem of complexity, we are primarily concerned with the amount of training data that algorithms require.

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Gao, Xi. "Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3942.

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Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel's law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today's scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, and train classification models that serve two goals: 1) class prediction for previously unseen samples; 2) knowledge discovery of the underlying causes of class differences. Application of our methods in genetic studies can help scientist take advantage of existing biological networks, generate diagnosis with higher accuracy, and discover the driver networks behind the differences. We proposed three new graph-based regularization algorithms. Graph Connectivity Constrained AdaBoost algorithm combines a connectivity module, a deletion function, and a model retraining procedure with the AdaBoost classifier. Graph-regularized Linear Programming Support Vector Machine integrates penalty term based on submodular graph cut function into linear classifier's objective function. Proximal Graph LogisticBoost adds lasso and graph-based penalties into logistic risk function of an ensemble classifier. Results of tests of our models on simulated biological datasets show that the proposed methods are able to produce accurate, sparse classifiers, and can help discover true genetic differences between phenotypes.

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Puthiya, Parambath Shameem Ahamed. "New methods for multi-objective learning." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2322/document.

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Les problèmes multi-objectifs se posent dans plusieurs scénarios réels dans le monde où on doit trouver une solution optimale qui soit un compromis entre les différents objectifs en compétition. Dans cette thèse, on étudie et on propose des algorithmes pour traiter les problèmes des machines d’apprentissage multi-objectif. On étudie deux méthodes d’apprentissage multi-objectif en détail. Dans la première méthode, on étudie le problème de trouver le classifieur optimal pour réaliser des mesures de performances multivariées. Dans la seconde méthode, on étudie le problème de classer des informations diverses dans les missions de recherche des informations
Multi-objective problems arise in many real world scenarios where one has to find an optimal solution considering the trade-off between different competing objectives. Typical examples of multi-objective problems arise in classification, information retrieval, dictionary learning, online learning etc. In this thesis, we study and propose algorithms for multi-objective machine learning problems. We give many interesting examples of multi-objective learning problems which are actively persuaded by the research community to motivate our work. Majority of the state of the art algorithms proposed for multi-objective learning comes under what is called “scalarization method”, an efficient algorithm for solving multi-objective optimization problems. Having motivated our work, we study two multi-objective learning tasks in detail. In the first task, we study the problem of finding the optimal classifier for multivariate performance measures. The problem is studied very actively and recent papers have proposed many algorithms in different classification settings. We study the problem as finding an optimal trade-off between different classification errors, and propose an algorithm based on cost-sensitive classification. In the second task, we study the problem of diverse ranking in information retrieval tasks, in particular recommender systems. We propose an algorithm for diverse ranking making use of the domain specific information, and formulating the problem as a submodular maximization problem for coverage maximization in a weighted similarity graph. Finally, we conclude that scalarization based algorithms works well for multi-objective learning problems. But when considering algorithms for multi-objective learning problems, scalarization need not be the “to go” approach. It is very important to consider the domain specific information and objective functions. We end this thesis by proposing some of the immediate future work, which are currently being experimented, and some of the short term future work which we plan to carry out

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He, Yuesheng. "The intelligent behavior of 3D graphical avatars based on machine learning methods." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1404.

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Sirin, Volkan. "Machine Learning Methods For Opponent Modeling In Games Of Imperfect Information." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614630/index.pdf.

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This thesis presents a machine learning approach to the problem of opponent modeling in games of imperfect information. The efficiency of various artificial intelligence techniques are investigated in this domain. A sequential game is called imperfect information game if players do not have all the information about the current state of the game. A very popular example is the Texas Holdem Poker, which is used for realization of the suggested methods in this thesis. Opponent modeling is the system that enables a player to predict the behaviour of its opponent. In this study, opponent modeling problem is approached as a classification problem. An architecture with different classifiers for each phase of the game is suggested. Neural Networks, K-Nearest Neighbors (KNN) and Support Vector Machines are used as classifier. For modeling a particular player, KNN is found to be most successful amongst all, with a prediction accuracy of 88%. An ensemble learning system is proposed for modeling different playing styles and unknown ones. Computational complexity and parallelization of some calculations are also provided.

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Wallis, David. "A study of machine learning and deep learning methods and their application to medical imaging." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST057.

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Nous utilisons d'abord des réseaux neuronaux convolutifs (CNNs) pour automatiser la détection des ganglions lymphatiques médiastinaux dans les images TEP/TDM. Nous construisons un modèle entièrement automatisé pour passer directement des images TEP/TDM à la localisation des ganglions. Les résultats montrent une performance comparable à celle d'un médecin. Dans la seconde partie de la thèse, nous testons la performance, l'interprétabilité et la stabilité des modèles radiomiques et CNN sur trois ensembles de données (IRM cérébrale 2D, TDM pulmonaire 3D, TEP/TDM médiastinale 3D). Nous comparons la façon dont les modèles s'améliorent lorsque davantage de données sont disponibles et nous examinons s'il existe des tendances communess aux différents problèmes. Nous nous demandons si les méthodes actuelles d'interprétation des modèles sont satisfaisantes. Nous étudions également comment une segmentation précise affecte les performances des modèles. Nous utilisons d'abord des réseaux neuronaux convolutifs (CNNs) pour automatiser la détection des ganglions lymphatiques médiastinaux dans les images TEP/TDM. Nous construisons un modèle entièrement automatisé pour passer directement des images TEP/TDM à la localisation des ganglions. Les résultats montrent une performance comparable à celle d'un médecin. Dans la seconde partie de la thèse, nous testons la performance, l'interprétabilité et la stabilité des modèles radiomiques et CNN sur trois ensembles de données (IRM cérébrale 2D, TDM pulmonaire 3D, TEP/TDM médiastinale 3D). Nous comparons la façon dont les modèles s'améliorent lorsque davantage de données sont disponibles et nous examinons s'il existe des tendances communess aux différents problèmes. Nous nous demandons si les méthodes actuelles d'interprétation des modèles sont satisfaisantes. Nous étudions également comment une segmentation précise affecte les performances des modèles
We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models. We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models

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Книги з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

G, Carbonell Jaime, ed. Machine learning: Paradigms and methods. Cambridge, Mass: MIT Press, 1990.

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Steven, Minton, and Symposium on Learning Methods for Planning Systems (1991 : Stanford University), eds. Machine learning methods for planning. San Mateo, Calif: M. Kaufmann, 1993.

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G, Bourbakis Nikolaos, ed. Applications of learning & planning methods. Singapore: World Scientific, 1991.

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Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. London: Springer London, 2013.

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J, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, Mass: MIT Press, 2002.

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Chang, Victor, Harleen Kaur, and Simon James Fong, eds. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04597-4.

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Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.

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Katharina, Morik, ed. Knowledge acquisition and machine learning: Theory, methods and applications / Katharina Morik ... [et al.]. London: Academic Press, 1993.

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service), SpringerLink (Online, ed. Criminal Justice Forecasts of Risk: A Machine Learning Approach. New York, NY: Springer New York, 2012.

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Léon-Charles, Tranchevent, Moor Bart, Moreau Yves, and SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Частини книг з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

Joshi, Ameet V. "Linear Methods." In Machine Learning and Artificial Intelligence, 33–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_4.

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Joshi, Ameet V. "Linear Methods." In Machine Learning and Artificial Intelligence, 45–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_5.

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Jovic, Alan, Dirmanto Jap, Louiza Papachristodoulou, and Annelie Heuser. "Traditional Machine Learning Methods for Side-Channel Analysis." In Security and Artificial Intelligence, 25–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98795-4_2.

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Baldi, Pierre. "Machine Learning Methods for Computational Proteomics and Beyond." In Advances in Artificial Intelligence, 8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44886-1_3.

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Anh, Nguyen Thi Ngoc, Tran Ngoc Thang, and Vijender Kumar Solanki. "Machine Learning and Ensemble Methods." In Artificial Intelligence for Automated Pricing Based on Product Descriptions, 9–18. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4702-4_2.

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Turtiainen, Hannu, Andrei Costin, and Timo Hämäläinen. "Defensive Machine Learning Methods and the Cyber Defence Chain." In Artificial Intelligence and Cybersecurity, 147–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15030-2_7.

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Turtiainen, Hannu, Andrei Costin, Alex Polyakov, and Timo Hämäläinen. "Offensive Machine Learning Methods and the Cyber Kill Chain." In Artificial Intelligence and Cybersecurity, 125–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15030-2_6.

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Ghosh, Shyamasree, and Rathi Dasgupta. "Introduction to Artificial Intelligence (AI) Methods in Biology." In Machine Learning in Biological Sciences, 19–27. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8881-2_2.

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Castanheira, José, Francisco Curado, Ana Tomé, and Edgar Gonçalves. "Machine Learning Methods for Radar-Based People Detection and Tracking." In Progress in Artificial Intelligence, 412–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30241-2_35.

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Iosifidis, Alexandros, Anastasios Tefas, and Ioannis Pitas. "Multi-view Regularized Extreme Learning Machine for Human Action Recognition." In Artificial Intelligence: Methods and Applications, 84–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07064-3_7.

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Тези доповідей конференцій з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

Lin, Weibo, Zhu He, and Mingyu Xiao. "Balanced Clustering: A Uniform Model and Fast Algorithm." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/414.

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Clustering is a fundamental research topic in data mining and machine learning. In addition, many specific applications demand that the clusters obtained be balanced. In this paper, we present a balanced clustering model that is to minimize the sum of squared distances to cluster centers, with uniform regularization functions to control the balance degree of the clustering results. To solve the model, we adopt the idea of the k-means method. We show that the k-means assignment step has an equivalent minimum cost flow formulation when the regularization functions are all convex. By using a novel and simple acceleration technique for the k-means and network simplex methods our model can be solved quite efficiently. Experimental results over benchmarks validate the advantage of our algorithm compared to the state-of-the-art balanced clustering algorithms. On most datasets, our algorithm runs more than 100 times faster than previous algorithms with a better solution.

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Lin, Jianxin, Yingce Xia, Yijun Wang, Tao Qin, and Zhibo Chen. "Image-to-Image Translation with Multi-Path Consistency Regularization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/413.

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Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.

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Liu, Chuanjian, Yunhe Wang, Kai Han, Chunjing Xu, and Chang Xu. "Learning Instance-wise Sparsity for Accelerating Deep Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/416.

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Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks. Most of existing approaches used to accelerate deep models by manipulating parameters or filters without data, e.g., pruning and decomposition. In contrast, we study this problem from a different perspective by respecting the difference between data. An instance-wise feature pruning is developed by identifying informative features for different instances. Specifically, by investigating a feature decay regularization, we expect intermediate feature maps of each instance in deep neural networks to be sparse while preserving the overall network performance. During online inference, subtle features of input images extracted by intermediate layers of a well-trained neural network can be eliminated to accelerate the subsequent calculations. We further take coefficient of variation as a measure to select the layers that are appropriate for acceleration. Extensive experiments conducted on benchmark datasets and networks demonstrate the effectiveness of the proposed method.

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Zhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. "DANE: Domain Adaptive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/606.

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Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.

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Pizarro, Jorge, Byron Vásquez, Willan Steven Mendieta Molina, and Remigio Hurtado. "Hepatitis predictive analysis model through deep learning using neural networks based on patient history." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001449.

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First of all, one of the applications of artificial intelligence is the prediction of diseases, including hepatitis. Hepatitis has been a recurring disease over the years as it seriously affects the population, increasing by 125,000 deaths per year. This process of inflammation and damage to the organ affects its performance, as well as the functioning of the other organs in the body. In this work, an analysis of variables and their influence on the objective variable is made, in addition, results are presented from a predictive model.We propose a predictive analysis model that incorporates artificial neural networks and we have compared this prediction method with other classification-oriented models such as support vector machines (SVM) and genetic algorithms. We have conducted our method as a classification problem. This method requires a prior process of data processing and exploratory analysis to identify the variables or factors that directly influence this type of disease. In this way, we will be able to identify the variables that intervene in the development of this disease and that affect the liver or the correct functioning of this organ, presenting discomfort to the human body, as well as complications such as liver failure or liver cancer. Our model is structured in the following steps: first, data extraction is performed, which was collected from the machine learning repository of the University of California at Irvine (UCI). Then these data go through a variable transformation process. Subsequently, it is processed with learning and optimization through a neural network. The optimization (fine-tuning) is performed in three phases: complication hyperparameter optimization, neural network layer density optimization, and finally dropout regularization optimization. Finally, the visualization and analysis of results is carried out. We have used a data set of patient medical records, among the variables are: age, sex, gender, hemoglobin, etc. We have found factors related either indirectly or directly to the disease. The results of the model are presented according to the quality measures: Recall, Precision and MAE.We can say that this research leaves the doors open to new challenges such as new implementations within the field of medicine, not only focused on the liver, but also being able to extend the development environment to other applications and organs of the human body in order to avoid risks possible, or future complications. It should be noted that the future of applications with the use of artificial neural networks is constantly evolving, the application of improved models such as the use of random forests, assembly algorithms show a great capacity for application both in biomedical engineering and in focused areas to the analysis of different types of medical images.

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Deksne, Daiga. "Chat Language Normalisation using Machine Learning Methods." In Special Session on Natural Language Processing in Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007693509650972.

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GARIP, Evin, and Ayse Betul OKTAY. "Forecasting CO2 Emission with Machine Learning Methods." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620767.

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Lin, Zizhao, and Yijiang Ma. "Machine learning methods in predicting electroencephalogram." In International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), edited by Lei Zhang, Siting Chen, and Mahmoud AlShawabkeh. SPIE, 2021. http://dx.doi.org/10.1117/12.2626522.

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Chen, Yi. "Driver fatigue detection using machine learning methods." In 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2022. http://dx.doi.org/10.1109/icaica54878.2022.9844425.

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Zainuddin, Nur Nadirah, Muhammad Sadiq Naim Bin Noor Azhari, Wahidah Hashim, Ammar Ahmed Alkahtani, Abdulsalam Salihu Mustafa, Gamal Alkawsi, and Fuad Noman. "Malaysian Coins Recognition Using Machine Learning Methods." In 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2021. http://dx.doi.org/10.1109/aidas53897.2021.9574175.

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Звіти організацій з теми "Machine Learning, Artificial Intelligence, Regularization Methods"

Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.

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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).

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