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Unbalanced Sequential Data in Machine Learning

Unbalanced Sequential Data in Machine Learning  eBooks & eLearning

Posted by naag at Sept. 27, 2017
Unbalanced Sequential Data in Machine Learning

Unbalanced Sequential Data in Machine Learning
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour | 204 MB
Genre: eLearning | Language: English
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods by Sarah Vluymans
English | PDF,EPUB | 2019 | 263 Pages | ISBN : 3030046621 | 13.85 MB

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches

Data Management in Machine Learning Systems  eBooks & eLearning

Posted by Underaglassmoon at April 18, 2019
Data Management in Machine Learning Systems

Data Management in Machine Learning Systems
Morgan & Claypool | English | 2019 | ISBN-10: 1681734982 | 174 pages | PDF | 2.17 MB

by Matthias Boehm (Author), Arun Kumar (Author), Jun Yang (Author)
Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications

Dataset Shift in Machine Learning  eBooks & eLearning

Posted by Jeembo at April 13, 2019
Dataset Shift in Machine Learning

Dataset Shift in Machine Learning by Joaquin Quiñonero-Candela
English | 2008 | ISBN: 0262170051 | 248 Pages | PDF | 4.6 MB

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.

A Course in Machine Learning by Hal Daume III  eBooks & eLearning

Posted by lengen at April 22, 2019
A Course in Machine Learning by Hal Daume III

A Course in Machine Learning by Hal Daume III
English | January 2017 | ISBN: N/A | 227 Pages | PDF | 3.7 MB

This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
Machine Learning: A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning

Machine Learning: A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning by Peter Bradley
English | December 24, 2018 | ASIN: B07MNMY81C | 79 pages | AZW3 | 0.80 MB

A First Course in Machine Learning, 2nd Edition  eBooks & eLearning

Posted by ksveta6 at Feb. 21, 2017
A First Course in Machine Learning, 2nd Edition

A First Course in Machine Learning, Second Edition (Machine Learning & Pattern Recognition) by Simon Rogers, Mark Girolami
2016 | ISBN: 1498738486 | English | 427 pages | PDF | 161 MB
Künstliche Intelligenz: Einführung in Machine Learning, Deep Learning, neuronale Netze, Robotik und Co

Künstliche Intelligenz: Einführung in Machine Learning, Deep Learning, neuronale Netze, Robotik und Co. by Phillip Kuhlmann
German | 18 Jun. 2018 | ISBN: 1983196061 | 85 Pages | MOBI | 268.97 KB

Advances in Machine Learning and Signal Processing  eBooks & eLearning

Posted by AlenMiler at July 15, 2016
Advances in Machine Learning and Signal Processing

Advances in Machine Learning and Signal Processing: Proceedings of MALSIP 2015 (Lecture Notes in Electrical Engineering) by Ping Jack Soh
English | 7 July 2016 | ISBN: 3319322125 | 324 Pages | PDF (True) | 9.6 MB

This book presents important research findings and recent innovations in the field of machine learning and signal processing.
Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning)

Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning) by Yoshua Bengio
English | Oct. 28, 2009 | ISBN: 1601982941 | 130 Pages | PDF | 1 MB

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae.