Relevant bibliographies by topics / Learning algorithm

Academic literature on the topic 'Learning algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Learning algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Learning algorithm"

1

Mu, Tong, Georgios Theocharous, David Arbour, and Emma Brunskill. "Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7841–49. http://dx.doi.org/10.1609/aaai.v36i7.20753.

Full text
Abstract:
Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. Our algorithm, Constraint Sampling Reinforcement Learning (CSRL), incorporates prior domain knowledge as constraints/restrictions on the RL policy. It takes in multiple potential policy constraints to maintain robustness to misspecification of individual constraints while leveraging helpful ones to learn quickly. Given a base RL learning algorithm (ex. UCRL, DQN, Rainbow) we propose an upper confidence with elimination scheme that leverages the relationship between the constraints, and their observed performance, to adaptively switch among them. We instantiate our algorithm with DQN-type algorithms and UCRL as base algorithms, and evaluate our algorithm in four environments, including three simulators based on real data: recommendations, educational activity sequencing, and HIV treatment sequencing. In all cases, CSRL learns a good policy faster than baselines.
APA, Harvard, Vancouver, ISO, and other styles
2

Note, Johan, and Maaruf Ali. "Comparative Analysis of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms." Annals of Emerging Technologies in Computing 6, no. 3 (July 1, 2022): 19–36. http://dx.doi.org/10.33166/aetic.2022.03.003.

Full text
Abstract:
Attacks against computer networks, “cyber-attacks”, are now common place affecting almost every Internet connected device on a daily basis. Organisations are now using machine learning and deep learning to thwart these types of attacks for their effectiveness without the need for human intervention. Machine learning offers the biggest advantage in their ability to detect, curtail, prevent, recover and even deal with untrained types of attacks without being explicitly programmed. This research will show the many different types of algorithms that are employed to fight against the different types of cyber-attacks, which are also explained. The classification algorithms, their implementation, accuracy and testing time are presented. The algorithms employed for this experiment were the Gaussian Naïve-Bayes algorithm, Logistic Regression Algorithm, SVM (Support Vector Machine) Algorithm, Stochastic Gradient Descent Algorithm, Decision Tree Algorithm, Random Forest Algorithm, Gradient Boosting Algorithm, K-Nearest Neighbour Algorithm, ANN (Artificial Neural Network) (here we also employed the Multilevel Perceptron Algorithm), Convolutional Neural Network (CNN) Algorithm and the Recurrent Neural Network (RNN) Algorithm. The study concluded that amongst the various machine learning algorithms, the Logistic Regression and Decision tree classifiers all took a very short time to be implemented giving an accuracy of over 90% for malware detection inside various test datasets. The Gaussian Naïve-Bayes classifier, though fast to implement, only gave an accuracy between 51-88%. The Multilevel Perceptron, non-linear SVM and Gradient Boosting algorithms all took a very long time to be implemented. The algorithm that performed with the greatest accuracy was the Random Forest Classification algorithm.
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Xinyu, Xiaoguang Gao, and Chenfeng Wang. "A Novel BN Learning Algorithm Based on Block Learning Strategy." Sensors 20, no. 21 (November 7, 2020): 6357. http://dx.doi.org/10.3390/s20216357.

Full text
Abstract:
Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated blocks. Then, a pruned dynamic programming algorithm is performed sequentially for all possible graph structures to get possible BNs and find the best BN by scoring function. Experiments show that for high-dimensional and sparse data, the BLMKM algorithm can achieve the same accuracy in a reasonable time compared with non-blocking classical learning algorithms. Compared to the existing block learning algorithms, the BLMKM algorithm has a time advantage on the basis of ensuring accuracy. The analysis of the real radar effect mechanism dataset proves that BLMKM algorithm can quickly establish a global and accurate causality model to find the cause of interference, predict the detecting result, and guide the parameters optimization. BLMKM algorithm is efficient for BN learning and has practical application value.
APA, Harvard, Vancouver, ISO, and other styles
4

Kumar Jitender Kumar, Yogesh. "Facemask Detection using Deep Learning Algorithm." International Journal of Science and Research (IJSR) 12, no. 5 (May 5, 2023): 1520–24. http://dx.doi.org/10.21275/sr23518151522.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lin, Ying Jian, and Xiao Ji Chen. "Simulated Annealing Algorithm Improved BP Learning Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 734–37. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.734.

Full text
Abstract:
BP learning algorithm has advantage of simple structure, easy to implement and so on, it has gained wide application in the malfunction diagnosis and pattern recognition etc.. For BP algorithm is easy to fall into local minima shortcoming cites simulated annealing algorithm. Firstly, study the basic idea of BP learning algorithm and its simple mathematical representation; Then, research simulated annealing algorithm theory and annealing processes; Finally, the study makes BP algorithm combine with simulated annealing algorithm to form a hybrid optimization algorithm of simulated annealing algorithm based on genetic and improved BP algorithm, and gives specific calculation steps. The results show that the content of this study give full play to their respective advantages of two algorithms, make best use of the advantages and bypass the disadvantages, whether in academic or in the application it has a very important significance.
APA, Harvard, Vancouver, ISO, and other styles
6

Ma, Jian Hua, and Fa Zhong Tian. "Intelligent Learning Ant Colony Algorithm." Applied Mechanics and Materials 48-49 (February 2011): 625–31. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.625.

Full text
Abstract:
Ant colony algorithm is effective algorithm for NP-hard problems, but it also tends to mature early as other evolutionary algorithms. One improvement method of ant colony algorithm is studied in this paper. Intelligent learning ant colony algorithm with the pheromone difference and positive-negative learning mechanism is brought forward to solve TSP. The basic approach of ant colony algorithm is introduced firstly, then we introduced the individual pheromone matrix and positive-negative learning mechanism into ant colony algorithm. Next the steps of intelligent learning ant colony algorithm are given. At last the effectiveness of this algorithm is proved by random numerical examples and typical numerical examples. It is also proved that intelligent ant and learning mechanism will affect concentration degree of pheromone.
APA, Harvard, Vancouver, ISO, and other styles
7

Coe, James, and Mustafa Atay. "Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms." Computers 10, no. 9 (September 10, 2021): 113. http://dx.doi.org/10.3390/computers10090113.

Full text
Abstract:
The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.
APA, Harvard, Vancouver, ISO, and other styles
8

Barbosa, Flávio, Arthur Vidal, and Flávio Mello. "Machine Learning for Cryptographic Algorithm Identification." Journal of Information Security and Cryptography (Enigma) 3, no. 1 (September 3, 2016): 3. http://dx.doi.org/10.17648/enig.v3i1.55.

Full text
Abstract:
This paper aims to study encrypted text files in order to identify their encoding algorithm. Plain texts were encoded with distinct cryptographic algorithms and then some metadata were extracted from these codifications. Afterward, the algorithm identification is obtained by using data mining techniques. Firstly, texts in Portuguese, English and Spanish were encrypted using DES, Blowfish, RSA, and RC4 algorithms. Secondly, the encrypted files were submitted to data mining techniques such as J48, FT, PART, Complement Naive Bayes, and Multilayer Perceptron classifiers. Charts were created using the confusion matrices generated in step two and it was possible to perceive that the percentage of identification for each of the algorithms is greater than a probabilistic bid. There are several scenarios where algorithm identification reaches almost 97, 23% of correctness.
APA, Harvard, Vancouver, ISO, and other styles
9

Yao, Jiajun. "RRT algorithm learning and optimization." Applied and Computational Engineering 53, no. 1 (March 28, 2024): 296–302. http://dx.doi.org/10.54254/2755-2721/53/20241614.

Full text
Abstract:
LWith the increasing maturity of RRT algorithm, more and more fields are starting to use this algorithm. For example, in path planning problems, this algorithm has been well applied because it has good performance and real-time performance. The RRT algorithm is a path planning algorithm based on tree structure. It continuously explores unknown regions, finds feasible paths, and ultimately connects the starting point and target point by randomly expanding the nodes of the tree. The RRT algorithm has good fast exploration ability and low computational complexity, making it suitable for path planning problems in various environments. This article focuses on studying the parameters in various RRT algorithms. Through analysis and comparison, more reasonable parameters were ultimately found. This article also involves optimizing and improving the RRT algorithm using the RRT * algorithm. The research in this article can further understand the application scenarios of the RRT algorithm. It is expected that this algorithm will be better applied in the field of autonomous driving in the future.
APA, Harvard, Vancouver, ISO, and other styles
10

Crandall, Jacob, Asad Ahmed, and Michael Goodrich. "Learning in Repeated Games with Minimal Information: The Effects of Learning Bias." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 650–56. http://dx.doi.org/10.1609/aaai.v25i1.7871.

Full text
Abstract:
Automated agents for electricity markets, social networks, and other distributed networks must repeatedly interact with other intelligent agents, often without observing associates' actions or payoffs (i.e., minimal information). Given this reality, our goal is to create algorithms that learn effectively in repeated games played with minimal information. As in other applications of machine learning, the success of a learning algorithm in repeated games depends on its learning bias. To better understand what learning biases are most successful, we analyze the learning biases of previously published multi-agent learning (MAL) algorithms. We then describe a new algorithm that adapts a successful learning bias from the literature to minimal information environments. Finally, we compare the performance of this algorithm with ten other algorithms in repeated games played with minimal information.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Learning algorithm"

1

Janagam, Anirudh, and Saddam Hossen. "Analysis of Network Intrusion Detection System with Machine Learning Algorithms (Deep Reinforcement Learning Algorithm)." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17126.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Patel, Darshan D. "Vehicle classification using machine learning algorithm." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1604876.

Full text
Abstract:

Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The generated tree model can then be easily implemented on microprocessors. The result of our experiment shows that the vehicle classification system is effective and efficient with the very high accuracy at ~98%.

APA, Harvard, Vancouver, ISO, and other styles
3

Cui, Yan Hong. "Contributions to statistical machine learning algorithm." Doctoral thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10284.

Full text
Abstract:
This thesis's research focus is on computational statistics along with DEAR (abbreviation of differential equation associated regression) model direction, and that in mind, the journal papers are written as contributions to statistical machine learning algorithm literature.
APA, Harvard, Vancouver, ISO, and other styles
4

Del, Ben Enrico <1997&gt. "Reinforcement Learning: a Q-Learning Algorithm for High Frequency Trading." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/20411.

Full text
Abstract:
The scope of this work is to test the implementation of an automated trading system based on Reinforcement Learning: a machine learning algorithm in which an intelligent agent acts to maximize its rewards given the environment around it. Indeed, given the environmental inputs and the environmental responses to the actions taken, the agent will learn how to behave in best way possible. In particular, in this work, a Q-Learning algorithm has been used to produce trading signals on the basis of high frequency data of the Limit Order Book for some selected stocks.
APA, Harvard, Vancouver, ISO, and other styles
5

Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

Find full text
Abstract:
Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
APA, Harvard, Vancouver, ISO, and other styles
6

El-Omari, Jawad A. "Efficient learning methods to tune algorithm parameters." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58890/.

Full text
Abstract:
This thesis focuses on the algorithm configuration problem. In particular, three efficient learning configurators are introduced to tune parameters offline. The first looks into metaoptimization, where the algorithm is expected to solve similar problem instances within varying computational budgets. Standard meta-optimization techniques have to be repeated whenever the available computational budget changes, as the parameters that work well for small budgets, may not be suitable for larger ones. The proposed Flexible Budget method can, in a single run, identify the best parameter setting for all possible computational budgets less than a specified maximum, without compromising solution quality. Hence, a lot of time is saved. This will be shown experimentally. The second regards Racing algorithms which often do not fully utilize the available computational budget to find the best parameter setting, as they may terminate whenever a single parameter remains in the race. The proposed Racing with reset can overcome this issue, and at the same time adapt Racing’s hyper-parameter α online. Experiments will show that such adaptation enables the algorithm to achieve significantly lower failure rates, compared to any fixed α set by the user. The third extends on Racing with reset by allowing it to utilize all the information gathered previously when it adapts α, it also permits Racing algorithms in general to intelligently allocate the budget in each iteration, as opposed to equally allocating it. All developed Racing algorithms are compared to two budget allocators from the Simulation Optimization literature, OCBA and CBA, and to equal allocation to demonstrate under which conditions each performs best in terms of minimizing the probability of incorrect selection.
APA, Harvard, Vancouver, ISO, and other styles
7

Murphy, Nicholas John. "An online learning algorithm for technical trading." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31048.

Full text
Abstract:
We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. [31] on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. [19]. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.
APA, Harvard, Vancouver, ISO, and other styles
8

O'Shea, Timothy James. "Learning from Data in Radio Algorithm Design." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/89649.

Full text
Abstract:
Algorithm design methods for radio communications systems are poised to undergo a massive disruption over the next several years. Today, such algorithms are typically designed manually using compact analytic problem models. However, they are shifting increasingly to machine learning based methods using approximate models with high degrees of freedom, jointly optimized over multiple subsystems, and using real-world data to drive design which may have no simple compact probabilistic analytic form. Over the past five years, this change has already begun occurring at a rapid pace in several fields. Computer vision tasks led deep learning, demonstrating that low level features and entire end-to-end systems could be learned directly from complex imagery datasets, when a powerful collection of optimization methods, regularization methods, architecture strategies, and efficient implementations were used to train large models with high degrees of freedom. Within this work, we demonstrate that this same class of end-to-end deep neural network based learning can be adapted effectively for physical layer radio systems in order to optimize for sensing, estimation, and waveform synthesis systems to achieve state of the art levels of performance in numerous applications. First, we discuss the background and fundamental tools used, then discuss effective strategies and approaches to model design and optimization. Finally, we explore a series of applications across estimation, sensing, and waveform synthesis where we apply this approach to reformulate classical problems and illustrate the value and impact this approach can have on several key radio algorithm design problems.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
9

Gunneström, Albert, and Erik Bauer. "Automating dataflow for a machine learning algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253068.

Full text
Abstract:
Machine learning algorithms can be used to predict the future demand for heat in buildings. This can be used as a decision basis by district heating plants when deciding an appropriate heat output for the plant. This project is based on an existing machine learning model that uses temperature data and the previous heat demand as input data. The model has to be able to make new predictions and display the results continuously in order to be useful for heating plant operators. In this project a program was developed that automatically collects input data, uses this data with the machine learning model and displays the predicted heat demand in a graph. One of the sources for input data does not always provide reliable data and in order to ensure that the program runs continuously and in a robust way, approximations of missing data have to be made. The result is a program that runs continuously but with some constraints on the input data. The input data needs to be able to provide some correct values within the last two days in order for the program run continuously. A comparison between calculated predictions and the actual measured heat demand showed that the predictions were in general higher than the actual values. Some possible causes and solutions were identified but are left for future work.
Maskininlärnings-algoritmer kan användas för att göra prediktioner på den framtida efterfrågan på värme i fastigheter. Detta kan användas som ett beslutsunderlag av fjärrvärmeverk för att avgöra en lämplig uteffekt. Detta projektarbete baseras på en befintlig maskininlärnings-modell som använder sig av temperaturdata och tidigare värmedata som inparametrar. Modellen måste kunna göra nya prediktioner och visa resultaten kontinuerligt för att vara användbar för driftpersonal på fjärrvärmeverk. I detta projekt utvecklades ett program som automatiskt samlar in inparameterdata, använder denna data i maskininlärnings-modellen och visar resultaten i en graf. En av källorna för inparameterdata ger inte alltid pålitlig data och för att garantera att programmet körs kontinuerligt och på ett robust vis så måste man approximera inkorrekt data. Resultatet är ett program som kör kontinuerligt men med några restriktioner på inparameterdatan. Inparameterdatan måste ha åtminstone några korrekta värden inom de senaste två dagarna för att programmet ska köras kontinuerligt. En jämförelse mellan beräknade prediktioner och den verkliga uppmätta efterfrågan på värme visade att prediktionerna generellt var högre än de verkliga värdena. Några möjliga orsaker och lösningar identifierades men lämnas till framtida arbeten.
APA, Harvard, Vancouver, ISO, and other styles
10

Cully, Antoine. "Creative Adaptation through Learning." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066664/document.

Full text
Abstract:
Les robots ont profondément transformé l’industrie manufacturière et sont susceptibles de délivrer de grands bénéfices pour la société, par exemple en intervenant sur des lieux de catastrophes naturelles, lors de secours à la personne ou dans le cadre de la santé et des transports. Ce sont aussi des outils précieux pour la recherche scientifique, comme pour l’exploration des planètes ou des fonds marins. L’un des obstacles majeurs à leur utilisation en dehors des environnements parfaitement contrôlés des usines ou des laboratoires, est leur fragilité. Alors que les animaux peuvent rapidement s’adapter à des blessures, les robots actuels ont des difficultés à faire preuve de créativité lorsqu’ils doivent surmonter un problème inattendu: ils sont limités aux capteurs qu’ils embarquent et ne peuvent diagnostiquer que les situations qui ont été anticipées par leur concepteurs. Dans cette thèse, nous proposons une approche différente qui consiste à laisser le robot apprendre de lui-même un comportement palliant la panne. Cependant, les méthodes actuelles d’apprentissage sont lentes même lorsque l’espace de recherche est petit et contraint. Pour surmonter cette limitation et permettre une adaptation rapide et créative, nous combinons la créativité des algorithmes évolutionnistes avec la rapidité des algorithmes de recherche de politique à travers trois contributions : les répertoires comportementaux, l’adaptation aux dommages et le transfert de connaissance entre plusieurs tâches. D’une manière générale, ces travaux visent à apporter les fondations algorithmiques permettant aux robots physiques d’être plus robustes, performants et autonomes
Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, for example in search and rescue, disaster response, health care, and transportation. They are also invaluable tools for scientific exploration of distant planets or deep oceans. A major obstacle to their widespread adoption in more complex environments and outside of factories is their fragility. While animals can quickly adapt to injuries, current robots cannot “think outside the box” to find a compensatory behavior when they are damaged: they are limited to their pre-specified self-sensing abilities, which can diagnose only anticipated failure modes and strongly increase the overall complexity of the robot. In this thesis, we propose a different approach that considers having robots learn appropriate behaviors in response to damage. However, current learning techniques are slow even with small, constrained search spaces. To allow fast and creative adaptation, we combine the creativity of evolutionary algorithms with the learning speed of policy search algorithms through three contributions: the behavioral repertoires, the damage recovery using these repertoires and the transfer of knowledge across tasks. Globally, this work aims to provide the algorithmic foundations that will allow physical robots to be more robust, effective and autonomous
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Learning algorithm"

1

Rao, R. Venkata. Teaching Learning Based Optimization Algorithm. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22732-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bhatia, Praveen. A learning algorithm for robotic assembly. Dearborn: Society of Manufacturing Engineers, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Analog algorithm: Landscapes of machine learning. Salzburg: Fotohof Edition, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Falkenhainer, Brian. The structure-mapping engine: Algorithm and examples. Urbana, Ill. (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lowton, Andrew David. A constructive learning algorithm based on back-propagation. Birmingham: Aston University. Department ofComputer Science and Applied Mathematics, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Blume, Christian. GLEAM - general learning evolutionary algorithm and method: Ein evolutionärer Algorithmus und seine Anwendungen. Karlsruhe: KIT Scientific Publ., 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Köpf, Christian Rudolf. Meta-learning: Strategies, implementations, and evaluations for algorithm selection. Berlin: Aka, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Mamduh Mustafa Awd, Mustafa. Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-40237-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Valdez, Fevrier, Juan Barraza, and Patricia Melin. Hybrid Competitive Learning Method Using the Fireworks Algorithm and Artificial Neural Networks. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47712-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Travis, Kerzic, and United States. National Aeronautics and Space Administration., eds. mGA1.0: A common LISP implementation of a messy genetic algorithm. [Houston, Tex.]: Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Learning algorithm"

1

Ding, Zihan. "Algorithm Table." In Deep Reinforcement Learning, 485–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ding, Zihan. "Algorithm Cheatsheet." In Deep Reinforcement Learning, 489–514. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Jo, Taeho. "EM Algorithm." In Machine Learning Foundations, 241–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65900-4_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kramer, Oliver. "Machine Learning." In Genetic Algorithm Essentials, 65–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52156-5_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Algorithm Evaluation." In Encyclopedia of Machine Learning, 35–36. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Covering Algorithm." In Encyclopedia of Machine Learning, 238. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_184.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Anytime Algorithm." In Encyclopedia of Machine Learning, 39. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Langford, John, Xinhua Zhang, Gavin Brown, Indrajit Bhattacharya, Lise Getoor, Thomas Zeugmann, Thomas Zeugmann, et al. "EM Algorithm." In Encyclopedia of Machine Learning, 311. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_247.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Apriori Algorithm." In Encyclopedia of Machine Learning, 39–40. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Lagoudakis, Michail G., Thomas Zeugmann, and Claude Sammut. "Viterbi Algorithm." In Encyclopedia of Machine Learning, 1025. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_878.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Learning algorithm"

1

B. Pio, P., L. P. F. Garcia, and A. Rivolli. "Meta-Learning Approach for Noise Filter Algorithm Recommendation." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227958.

Full text
Abstract:
Preprocessing techniques can increase the quality or even enable Machine Learning algorithms. However, it is not simple to identify the preprocessing algorithms we should apply. This work proposes a methodology to recommend a noise filtering algorithm based on Meta-Learning, predicting which algorithm should be chosen based on a set of features calculated from a dataset. From synthetics datasets, we created the meta-data from an extracted set of meta-features and the f1-score performance metric calculated from the DT, KNN, and RF classifiers. To perform the suggestion, we used a meta-ranker that returns the rank of the best algorithms. We selected three noise filtering algorithms, HARF, GE, and ORBoost. To predict the f1-score, we used the PCT, RF, and KNN algorithms as meta-rankers. Our results indicate that the proposed solution acquired over 60% and 80% accuracy when considering a top-1 and top-2 approach. It also shows that the meta-rankers, when compared with a random choice and single algorithms as a baseline, provided an overall performance gain for the Machine Learning algorithm.
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Guokai, Liang Gao, Weiming Shen, and Andrew Kusiak. "A Broad Transfer Learning Algorithm for Classification of Bearing Faults." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8312.

Full text
Abstract:
Abstract Condition monitoring and fault diagnosis are of great interest to the manufacturing industry. Deep learning algorithms have shown promising results in equipment prognostics and health management. However, their success has been hindered by excessive training time. In addition, deep learning algorithms face the domain adaptation dilemma encountered in dynamic application environments. The emerging concept of broad learning addresses the training time and the domain adaptation issue. In this paper, a broad transfer learning algorithm is proposed for the classification of bearing faults. Data of the same frequency is used to construct one- and two-dimensional training data sets to analyze performance of the broad transfer and deep learning algorithms. A broad learning algorithm contains two main layers, an augmented feature layer and a classification layer. The broad learning algorithm with a sparse auto-encoder is employed to extract features. The optimal solution of a redefined cost function with a limited sample size to ten per class in the target domain offers the classifier of broad learning domain adaptation capability. The effectiveness of the proposed algorithm has been demonstrated on a benchmark dataset. Computational experiments have demonstrated superior efficiency and accuracy of the proposed algorithm over the deep learning algorithms tested.
APA, Harvard, Vancouver, ISO, and other styles
3

Pereira, Gean T. "Transfer Learning for Algorithm Recommendation." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120836.

Full text
Abstract:
Meta-Learning is a subarea of Machine Learning that aims to take advantage of prior knowledge to learn faster and with fewer data. There are different scenarios where meta-learning can be applied, and one of the most common is algorithm recommendation, where previous experience on applying machine learning algorithms for several datasets can be used to learn which algorithm, from a set of options, would be more suitable for a new dataset. Perhaps the most popular form of meta-learning is transfer learning, which consists of transferring knowledge acquired by a machine learning algorithm in a previous learning task to increase its performance faster in another and similar task. Transfer Learning has been widely applied in a variety of complex tasks such as image classification, machine translation and speech recognition, achieving remarkable results. Although transfer learning is very used in traditional or base-learning, it is still unknown if it is useful in a meta-learning setup. For that purpose, in this paper, we investigate the effects of transferring knowledge in the meta-level instead of base-level. Thus, we train a neural network on meta-datasets related to algorithm recommendation, and then using transfer learning, we reuse the knowledge learned by the neural network in other similar datasets from the same domain, to verify how transferable is the acquired meta-knowledge.
APA, Harvard, Vancouver, ISO, and other styles
4

Morris, Christopher, Matthias Fey, and Nils Kriege. "The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/618.

Full text
Abstract:
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine learning setting. We discuss the theoretical background, show how to use it for supervised graph- and node classification, discuss recent extensions, and its connection to neural architectures. Moreover, we give an overview of current applications and future directions to stimulate research.
APA, Harvard, Vancouver, ISO, and other styles
5

Dong, Li-yan, Guang-yuan Liu, Sen-miao Yuan, Yong-li Li, and Zhen Li. "Classifier Learning Algorithm Based on Genetic Algorithms." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.214.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mitev, Mitko, Ivaylo Plamenov, and Anatolii Antonov. "ALGORITHM FOR DECOMPOSITION OF LEARNING CONTENT." In eLSE 2012. Editura Universitara, 2012. http://dx.doi.org/10.12753/2066-026x-12-125.

Full text
Abstract:
One of the basic problems in the preparation of distant learning courses is dividing the content into learning units. In methodical point of view the requirements are associated with formation of learning units, based on strong connected notions, minimization of the links between them and their “orientation” on the time axis. Analogically the basic notions should precede their consequences in time. The formalization of this problem is reduced to finite, oriented graph, presenting interpretation of the main notions and the established causal links between them. The learning content division is obtained as a result of the graph decomposition. A target function is defined, based on the minimal value of the links between the terms and the corresponding interdisciplinary links. Limiting conditions are set, forming the volume of the educational units. The limitations are defined in two aspects: static in the case of group education and dynamic in dependence with the individual capabilities of the learning person. An algorithm for the so called “parallel” decomposition is developed. An estimation of its calculating complexity and performance is presented. The results are compared to corresponding “sequential” algorithm. The algorithm is included into the group of a set of algorithms for learning content structuring for optimization of the input-output links of the subject, for testing the knowledge and the obtained skills, for additional education, etc. The programming realization of the algorithm is researched by generating set of learning structures. Decomposition in the case of static and dynamic limitations is performed. The results could be used as an instrumental tool for learning units forming based on the subject area and defined parameters of the distant learning or to be included as extension of the known platforms for distant learning.
APA, Harvard, Vancouver, ISO, and other styles
7

Arden, Farel, and Cutifa Safitri. "Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms." In 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2022. http://dx.doi.org/10.1109/icitisee57756.2022.10057630.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gao, X., S. Ovaska, and X. Wang. "Genetic Algorithms-based Detector Generation in Negative Selection Algorithm." In 2006 IEEE Mountain Workshop on Adaptive and Learning Systems. IEEE, 2006. http://dx.doi.org/10.1109/smcals.2006.250704.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Chug, A., and S. Dhall. "Software Defect Prediction Using Supervised Learning Algorithm and Unsupervised Learning Algorithm." In Confluence 2013: The Next Generation Information Technology Summit (4th International Conference). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2313.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kayama, Mizue, Takashi Nagai, Hisayoshi Kunimune, Masaaki Niimura, Rika Kayatsu, and Yasushi Fuwa. "Algorithm element controllable tool for algorithmic thinking learning." In 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE). IEEE, 2013. http://dx.doi.org/10.1109/tale.2013.6654445.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Learning algorithm"

1

Mitchell, Wayne, Josh Kallman, Allen Toreja, Brian Gallagher, Ming Jiang, and Dan Laney. Developing a Learning Algorithm-Generated Empirical Relaxer. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1248278.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Toskova, Asya, Borislav Toshkov, Stanimir Stoyanov, and Ivan Popchev. Genetic Algorithm for a Learning Humanoid Robot. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2019. http://dx.doi.org/10.7546/crabs.2019.08.13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wang, Fulton, and Ali Pinar. Developing an Active Learning algorithm for learning Bayesian classifiers under the Multiple Instance Learning scenario. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1821545.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

Full text
Abstract:
As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
APA, Harvard, Vancouver, ISO, and other styles
5

Deller, Jr, Hunt J. R., and S. D. A Simple 'Linearized' Learning Algorithm Which Outperforms Back-Propagation. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada249697.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Church, Joshua, LaKenya Walker, and Amy Bednar. Iterative Learning Algorithm for Records Analysis (ILARA) user manual. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41845.

Full text
Abstract:
This manual is intended for new users with minimal or no experience with using the Iterative Learning Algorithm for Records Analysis (ILARA) tool. The goal of this document is to give an overview of the main functions of ILARA. The primary focus of this document is to demonstrate functionality. Every effort has been made to ensure this document is an accurate representation of the functionality of the ILARA tool. For additional information about this manual, contact ERDC.JAIC@erdc.dren.mil.
APA, Harvard, Vancouver, ISO, and other styles
7

Veals, Jeffrey, and Christopher Stone. Chemical Kinetics Database Translation for Machine-Learning-Based Algorithm Development. DEVCOM Army Research Laboratory, October 2023. http://dx.doi.org/10.21236/ad1182193.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Warner, Andrew D. Low Level Segmentation for Imitation Learning Using the Expectation Maximization Algorithm. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada460525.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Cui, Yonggang. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1477475.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Cui, Y. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1413952.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Learning algorithm' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography