Proceedings Of The International Conference On Machine Learning
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Icml 2004
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Author : Russell Greiner
language : en
Publisher:
Release Date : 2004
Icml 2004 written by Russell Greiner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computer science categories.
Machine Learning And Big Data Analytics Proceedings Of International Conference On Machine Learning And Big Data Analytics Icmlbda 2021
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Author : Rajiv Misra
language : en
Publisher: Springer Nature
Release Date : 2021-09-29
Machine Learning And Big Data Analytics Proceedings Of International Conference On Machine Learning And Big Data Analytics Icmlbda 2021 written by Rajiv Misra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-29 with Computers categories.
This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.
Icdsmla 2020
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Author : Amit Kumar
language : en
Publisher: Springer Nature
Release Date : 2021-11-08
Icdsmla 2020 written by Amit Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Technology & Engineering categories.
This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.
Proceedings Of International Conference On Big Data Machine Learning And Applications
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Author : Ripon Patgiri
language : en
Publisher: Springer Nature
Release Date : 2021-03-22
Proceedings Of International Conference On Big Data Machine Learning And Applications written by Ripon Patgiri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-22 with Technology & Engineering categories.
This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applications in the broad areas of big data and machine learning. It brings together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings.
Proceedings Of International Conference On Artificial Intelligence And Applications
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Author : Poonam Bansal
language : en
Publisher:
Release Date : 2021
Proceedings Of International Conference On Artificial Intelligence And Applications written by Poonam Bansal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Artificial intelligence categories.
This book gathers high-quality papers presented at the International Conference on Artificial Intelligence and Applications (ICAIA 2020), held at Maharaja Surajmal Institute of Technology, New Delhi, India, on 6-7 February 2020. The book covers areas such as artificial neural networks, fuzzy systems, computational optimization technologies and machine learning.
Machine Learning
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Author : Armand Prieditis
language : en
Publisher: Morgan Kaufmann
Release Date : 1995
Machine Learning written by Armand Prieditis and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
Machine Learning Proceedings 1995.
Machine Learning
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Author : Derek Sleeman
language : en
Publisher:
Release Date : 1992
Machine Learning written by Derek Sleeman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.
Proceedings Of The Twenty Third International Conference On Machine Learning
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Author : William W. Cohen
language : en
Publisher:
Release Date : 2006
Proceedings Of The Twenty Third International Conference On Machine Learning written by William W. Cohen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Artificial intelligence categories.
Encyclopedia Of Machine Learning
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Author : Claude Sammut
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28
Encyclopedia Of Machine Learning written by Claude Sammut and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Computers categories.
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Machine Learning
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Author : Sergios Theodoridis
language : en
Publisher: Academic Press
Release Date : 2020-02-19
Machine Learning written by Sergios Theodoridis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-19 with Technology & Engineering categories.
Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: - Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). - Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes. - Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method - Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling - Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more