Littérature scientifique sur le sujet « UCI MACHINE LEARNING »
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Articles de revues sur le sujet "UCI MACHINE LEARNING"
Aprianto, Kasiful. « Heart Disease UCI Machine Learning ». JITCE (Journal of Information Technology and Computer Engineering) 5, no 02 (30 septembre 2021) : 70–74. http://dx.doi.org/10.25077/jitce.5.02.70-74.2021.
Texte intégralMohammad, Ahmad Saeed, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani et Jonathon A. Chambers. « Comprehensive Evaluations of Student Performance Estimation via Machine Learning ». Mathematics 11, no 14 (18 juillet 2023) : 3153. http://dx.doi.org/10.3390/math11143153.
Texte intégralTURAN, SELIN CEREN, et MEHMET ALI CENGIZ. « ENSEMBLE LEARNING ALGORITHMS ». Journal of Science and Arts 22, no 2 (30 juin 2022) : 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.
Texte intégralVranjković, Vuk S., Rastislav J. R. Struharik et Ladislav A. Novak. « Reconfigurable Hardware for Machine Learning Applications ». Journal of Circuits, Systems and Computers 24, no 05 (8 avril 2015) : 1550064. http://dx.doi.org/10.1142/s0218126615500644.
Texte intégralKibria, Md Golam, et Mehmet Sevkli. « Application of Deep Learning for Credit Card Approval : A Comparison with Two Machine Learning Techniques ». International Journal of Machine Learning and Computing 11, no 4 (août 2021) : 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.
Texte intégralAnderies, Anderies, Jalaludin Ar Raniry William Tchin, Prambudi Herbowo Putro, Yudha Putra Darmawan et Alexander Agung Santoso Gunawan. « Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms ». Engineering, MAthematics and Computer Science (EMACS) Journal 4, no 3 (30 septembre 2022) : 87–93. http://dx.doi.org/10.21512/emacsjournal.v4i3.8683.
Texte intégralVerma, Raunak, Shashank Tandon et Mr Vinayak. « Heart Disease Prediction using Machine Learning ». International Journal for Research in Applied Science and Engineering Technology 10, no 5 (31 mai 2022) : 1872–76. http://dx.doi.org/10.22214/ijraset.2022.42687.
Texte intégralHamed, Samer, Abdelwadood Mesleh et Abdullah Arabiyyat. « Breast Cancer Detection Using Machine Learning Algorithms ». International Journal of Computer Science and Mobile Computing 10, no 11 (30 novembre 2021) : 4–11. http://dx.doi.org/10.47760/ijcsmc.2021.v10i11.002.
Texte intégralAlnemari, Shouq, et Majid Alshammari. « Detecting Phishing Domains Using Machine Learning ». Applied Sciences 13, no 8 (7 avril 2023) : 4649. http://dx.doi.org/10.3390/app13084649.
Texte intégralSuneetha Rani R, Gayathri B, Venkata Surya M, Jharani Asha Kiran P et Siva Krishna R. « Detecting counterfeit banknotes with machine learning ». South Asian Journal of Engineering and Technology 12, no 3 (11 juillet 2022) : 146–51. http://dx.doi.org/10.26524/sajet.2022.12.40.
Texte intégralThèses sur le sujet "UCI MACHINE LEARNING"
Modi, Navikkumar. « Machine Learning and Statistical Decision Making for Green Radio ». Thesis, CentraleSupélec, 2017. http://www.theses.fr/2017SUPL0002/document.
Texte intégralFuture cellular network technologies are targeted at delivering self-organizable and ultra-high capacity networks, while reducing their energy consumption. This thesis studies intelligent spectrum and topology management through cognitive radio techniques to improve the capacity density and Quality of Service (QoS) as well as to reduce the cooperation overhead and energy consumption. This thesis investigates how reinforcement learning can be used to improve the performance of a cognitive radio system. In this dissertation, we deal with the problem of opportunistic spectrum access in infrastructureless cognitive networks. We assume that there is no information exchange between users, and they have no knowledge of channel statistics and other user's actions. This particular problem is designed as multi-user restless Markov multi-armed bandit framework, in which multiple users collect a priori unknown reward by selecting a channel. The main contribution of the dissertation is to propose a learning policy for distributed users, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB (RQoS-UCB), achieves at most logarithmic order regret. Moreover, numerical studies show that the performance of the cognitive radio system can be significantly enhanced by utilizing proposed learning policies since the cognitive devices are able to identify the appropriate resources more efficiently. This dissertation also introduces a reinforcement learning and transfer learning frameworks to improve the energy efficiency (EE) of the heterogeneous cellular network. Specifically, we formulate and solve an energy efficiency maximization problem pertaining to dynamic base stations (BS) switching operation, which is identified as a combinatorial learning problem, with restless Markov multi-armed bandit framework. Furthermore, a dynamic topology management using the previously defined algorithm, RQoS-UCB, is introduced to intelligently control the working modes of BSs, based on traffic load and capacity in multiple cells. Moreover, to cope with initial reward loss and to speed up the learning process, a transfer RQoS-UCB policy, which benefits from the transferred knowledge observed in historical periods, is proposed and provably converges. Then, proposed dynamic BS switching operation is demonstrated to reduce the number of activated BSs while maintaining an adequate QoS. Extensive numerical simulations demonstrate that the transfer learning significantly reduces the QoS fluctuation during traffic variation, and it also contributes to a performance jump-start and presents significant EE improvement under various practical traffic load profiles. Finally, a proof-of-concept is developed to verify the performance of proposed learning policies on a real radio environment and real measurement database of HF band. Results show that proposed multi-armed bandit learning policies using dual criterion (e.g. availability and quality) optimization for opportunistic spectrum access is not only superior in terms of spectrum utilization but also energy efficient
Duncan, Andrew Paul. « The analysis and application of artificial neural networks for early warning systems in hydrology and the environment ». Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.
Texte intégralBouneffouf, Djallel. « DRARS, A Dynamic Risk-Aware Recommender System ». Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-01026136.
Texte intégralFanciulli, Matteo. « Forecast sull'impatto della crescita esponenziale della tecnologia nel mondo del lavoro e nella società ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
Trouver le texte intégralvan, Merriënboer Bart. « Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools ». Thèse, 2018. http://hdl.handle.net/1866/21743.
Texte intégralAskari, Hemmat Reyhane. « SLA violation prediction : a machine learning perspective ». Thèse, 2016. http://hdl.handle.net/1866/18754.
Texte intégralMokaddem, Mouna. « Learning a graph made of boolean function nodes : a new approach in machine learning ». Thèse, 2016. http://hdl.handle.net/1866/18763.
Texte intégralIn this document we present a novel approach in machine learning for classification. The framework we propose is based on boolean circuits, more specifically the classifier produced by our algorithm has that form. Using bits and boolean gates enable the learning algorithm and the classifier to use very efficient boolean vector operations. The accuracy of the classifier we obtain with our framework compares very favourably with those produced by conventional techniques, both in terms of efficiency and accuracy. Furthermore, the framework can be used in a context where information privacy is a necessity, for example we can classify private data. This can be done because computation can be performed only through boolean circuits as encrypted data is quantized in bits. Moreover, assuming that the classifier was trained, it can then be easily implemented on FPGAs (i.e., Field-programmable gate array) as those circuits are also based on logic gates and bitwise operations. Therefore, our model can be easily integrated in real-time classification systems.
Chapados, Nicolas. « Sequential Machine learning Approaches for Portfolio Management ». Thèse, 2009. http://hdl.handle.net/1866/3578.
Texte intégralThis thesis considers a number of approaches to make machine learning algorithms better suited to the sequential nature of financial portfolio management tasks. We start by considering the problem of the general composition of learning algorithms that must handle temporal learning tasks, in particular that of creating and efficiently updating the training sets in a sequential simulation framework. We enumerate the desiderata that composition primitives should satisfy, and underscore the difficulty of rigorously and efficiently reaching them. We follow by introducing a set of algorithms that accomplish the desired objectives, presenting a case-study of a real-world complex learning system for financial decision-making that uses those techniques. We then describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-best paths search algorithm. We consider an application in financial portfolio management where we train a learning algorithm to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function. We illustrate the approach by demonstrating extensive experimental results using a neural network architecture specialized for portfolio management and compare against well-known alternatives. Finally, we introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.
Gidel, Gauthier. « Multi-player games in the era of machine learning ». Thesis, 2020. http://hdl.handle.net/1866/24800.
Texte intégralAmong all the historical board games played by humans, the game of go was considered one of the most difficult to master by a computer program [Van Den Heriket al., 2002]; Until it was not [Silver et al., 2016]. This odds-breaking break-through [Müller, 2002, Van Den Herik et al., 2002] came from a sophisticated combination of Monte Carlo tree search and machine learning techniques to evaluate positions, shedding light upon the high potential of machine learning to solve games. Adversarial training, a special case of multiobjective optimization, is an increasingly useful tool in machine learning. For example, two-player zero-sum games are important for generative modeling (GANs) [Goodfellow et al., 2014] and mastering games like Go or Poker via self-play [Silver et al., 2017, Brown and Sandholm,2017]. A classic result in Game Theory states that convex-concave games always have an equilibrium [Neumann, 1928]. Surprisingly, machine learning practitioners successfully train a single pair of neural networks whose objective is a nonconvex-nonconcave minimax problem while for such a payoff function, the existence of a Nash equilibrium is not guaranteed in general. This work is an attempt to put learning in games on a firm theoretical foundation. The first contribution explores minimax theorems for a particular class of nonconvex-nonconcave games that encompasses generative adversarial networks. The proposed result is an approximate minimax theorem for two-player zero-sum games played with neural networks, including WGAN, StarCrat II, and Blotto game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, the payoff of these games are concave-convex with respect to the actual functions (or distributions) parametrized by these neural networks. The second and third contributions study the optimization of minimax problems, and more generally, variational inequalities in the context of machine learning. While the standard gradient descent-ascent method fails to converge to the Nash equilibrium of simple convex-concave games, there exist ways to use gradients to obtain methods that converge. We investigate several techniques such as extrapolation, averaging and negative momentum. We explore these techniques experimentally by proposing a state-of-the-art (at the time of publication) optimizer for GANs called ExtraAdam. We also prove new convergence results for Extrapolation from the past, originally proposed by Popov [1980], as well as for gradient method with negative momentum. The fourth contribution provides an empirical study of the practical landscape of GANs. In the second and third contributions, we diagnose that the gradient method breaks when the game’s vector field is highly rotational. However, such a situation may describe a worst-case that does not occur in practice. We provide new visualization tools in order to exhibit rotations in practical GAN landscapes. In this contribution, we show empirically that the training of GANs exhibits significant rotations around Local Stable Stationary Points (LSSP), and we provide empirical evidence that GAN training converges to a stable stationary point, which is a saddle point for the generator loss, not a minimum, while still achieving excellent performance.
Dauphin, Yann. « Advances in scaling deep learning algorithms ». Thèse, 2015. http://hdl.handle.net/1866/13710.
Texte intégralChapitres de livres sur le sujet "UCI MACHINE LEARNING"
Collaris, Dennis, Pratik Gajane, Joost Jorritsma, Jarke J. van Wijk et Mykola Pechenizkiy. « LEMON : Alternative Sampling for More Faithful Explanation Through Local Surrogate Models ». Dans Advances in Intelligent Data Analysis XXI, 77–90. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30047-9_7.
Texte intégralAbdalla, Hassan I. « A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets ». Dans Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 623–32. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_64.
Texte intégralLorenzi, Marco, Marie Deprez, Irene Balelli, Ana L. Aguila et Andre Altmann. « Integration of Multimodal Data ». Dans Machine Learning for Brain Disorders, 573–97. New York, NY : Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_19.
Texte intégralXiao, Yuteng, Hongsheng Yin, Kaijian Xia, Yundong Zhang et Honggang Qi. « Utilization of CNN-LSTM Model in Prediction of Multivariate Time Series for UCG ». Dans Machine Learning for Cyber Security, 429–40. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7_40.
Texte intégralWang, Kai, Bryan Wilder, Sze-chuan Suen, Bistra Dilkina et Milind Tambe. « Improving GP-UCB Algorithm by Harnessing Decomposed Feedback ». Dans Machine Learning and Knowledge Discovery in Databases, 555–69. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_44.
Texte intégralGong, Pixin, Xiaoran Huang, Chenyu Huang et Shiliang Wang. « Modeling on Outdoor Thermal Comfort in Traditional Residential Neighborhoods in Beijing Based on GAN ». Dans Computational Design and Robotic Fabrication, 273–83. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8405-3_23.
Texte intégralShafi, Mujtaba, Amit Jain et Majid Zaman. « Applying Machine Learning Algorithms on Urban Heat Island (UHI) Dataset ». Dans International Conference on Innovative Computing and Communications, 725–32. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3679-1_63.
Texte intégralDong, W., Y. Huang, B. Lehane et G. Ma. « An Intelligent Multi-objective Design Optimization Method for Nanographite-Based Electrically Conductive Cementitious Composites ». Dans Lecture Notes in Civil Engineering, 339–46. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_35.
Texte intégralKochupillai, Mrinalini, et Julia Köninger. « Creating a Digital Marketplace for Agrobiodiversity and Plant Genetic Sequence Data : Legal and Ethical Considerations of an AI and Blockchain Based Solution ». Dans Towards Responsible Plant Data Linkage : Data Challenges for Agricultural Research and Development, 223–53. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13276-6_12.
Texte intégralB., Shiva Shanta Mani, et Manikandan V. M. « Heart Disease Prediction Using Machine Learning ». Dans Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 373–81. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch018.
Texte intégralActes de conférences sur le sujet "UCI MACHINE LEARNING"
Eklund, Peter W. « Comparative study of public-domain supervised machine-learning accuracy on the UCI database ». Dans AeroSense '99, sous la direction de Belur V. Dasarathy. SPIE, 1999. http://dx.doi.org/10.1117/12.339989.
Texte intégralDas, Purnima, John F. Roddick, Patricia A. H. Williams et Mehwish Nasim. « Optimised Association Rule Mining for Health Data ». Dans 5th International Conference on Machine Learning & Applications. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131007.
Texte intégralGrandhi, Appalaraju, et Sunil Kumar Singh. « Performance Evaluation and Comparative Study of Machine Learning Techniques on UCI Datasets and Microarray Datasets ». Dans 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023. http://dx.doi.org/10.1109/icoei56765.2023.10125849.
Texte intégralSetiyani, Lila, Ayu Indahsari, Rosalina et Tjong Wansen. « Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case Study UCI Machine Learning Dataset) ». Dans 2022 IEEE International Conference on Sustainable Engineering and Creative Computing (ICSECC). IEEE, 2022. http://dx.doi.org/10.1109/icsecc56055.2022.10331379.
Texte intégralJik Lee, Byoung. « Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning ». Dans 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101608.
Texte intégralKaur, Simarjeet, Meenakshi Bansal et Ashok Kumar Bathla. « A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques ». Dans International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.56.
Texte intégralThumpati, Asitha, et Yan Zhang. « Towards Optimizing Performance of Machine Learning Algorithms on Unbalanced Dataset ». Dans 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131914.
Texte intégralWang, Nan, Xibin Zhao, Yu Jiang et Yue Gao. « Iterative Metric Learning for Imbalance Data Classification ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/389.
Texte intégralSen, Anupam. « Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset ». Dans Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.5.
Texte intégralSakai, Hiroshi, Chenxi Liu et Michinori Nakata. « Information Dilution : Granule-Based Information Hiding in Table Data - A Case of Lenses Data Set in UCI Machine Learning Repository ». Dans 2016 Third International Conference on Computing Measurement Control and Sensor Network (CMCSN). IEEE, 2016. http://dx.doi.org/10.1109/cmcsn.2016.28.
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