Advances in Independent Component Analysis and Learning Machines

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithm Unsupervised deep learning Machine vision and image retrieval A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning. A diverse set of application fields, ranging from machine vision to science policy data. Contributions from leading researchers in the field.

Produk Detail:

  • Author : Ella Bingham
  • Publisher : Academic Press
  • Pages : 328 pages
  • ISBN : 0128028076
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKAdvances in Independent Component Analysis and Learning Machines

Advances in Independent Component Analysis and Learning Machines

Advances in Independent Component Analysis and Learning Machines
  • Author : Ella Bingham,Samuel Kaski,Jorma Laaksonen,Jouko Lampinen
  • Publisher : Academic Press
  • Release : 14 May 2015
GET THIS BOOKAdvances in Independent Component Analysis and Learning Machines

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithm Unsupervised deep

Recent Advances in Big Data and Deep Learning

Recent Advances in Big Data and Deep Learning
  • Author : Luca Oneto,Nicolò Navarin,Alessandro Sperduti,Davide Anguita
  • Publisher : Springer
  • Release : 02 April 2019
GET THIS BOOKRecent Advances in Big Data and Deep Learning

This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In

Biometrics—Advances in Research and Application: 2013 Edition

Biometrics—Advances in Research and Application: 2013 Edition
  • Author : Anonim
  • Publisher : ScholarlyEditions
  • Release : 21 June 2013
GET THIS BOOKBiometrics—Advances in Research and Application: 2013 Edition

Biometrics—Advances in Research and Application: 2013 Edition is a ScholarlyBrief™ that delivers timely, authoritative, comprehensive, and specialized information about ZZZAdditional Research in a concise format. The editors have built Biometrics—Advances in Research and Application: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about ZZZAdditional Research in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Biometrics—Advances in Research

Advances in Web-Based Learning

Advances in Web-Based Learning
  • Author : Joseph Fong,Chu Ting Cheung,Hong Va Leong,Qing Li
  • Publisher : Springer
  • Release : 02 August 2003
GET THIS BOOKAdvances in Web-Based Learning

This book constitutes the refereed proceedings of the First International Conference on Web-Based Learning, ICWL 2002, held in Hong Kong, China in August 2002.The 34 revised full papers presented together with an invited keynote paper were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on system modeling and architectures, distance learning systems engineering, collaborative systems, experiences in distance learning, databases and data mining, and multimedia.

Advances in Machine Learning and Cybernetics

Advances in Machine Learning and Cybernetics
  • Author : Daniel S. Yeung,Zhi-Qiang Liu,Xi-Zhao Wang,Hong Yan
  • Publisher : Springer Science & Business Media
  • Release : 18 April 2006
GET THIS BOOKAdvances in Machine Learning and Cybernetics

This book constitutes the thoroughly refereed post-proceedings of the 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, held in Guangzhou, China in August 2005. The 114 revised full papers of this volume are organized in topical sections on agents and distributed artificial intelligence, control, data mining and knowledge discovery, fuzzy information processing, learning and reasoning, machine learning applications, neural networks and statistical learning methods, pattern recognition, vision and image processing.

Advances in Independent Component Analysis

Advances in Independent Component Analysis
  • Author : Mark Girolami
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKAdvances in Independent Component Analysis

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year. It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the

Source Separation and Machine Learning

Source Separation and Machine Learning
  • Author : Jen-Tzung Chien
  • Publisher : Academic Press
  • Release : 01 November 2018
GET THIS BOOKSource Separation and Machine Learning

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and

Advances in Machine Learning

Advances in Machine Learning
  • Author : Zhi-Hua Zhou,Takashi Washio
  • Publisher : Springer Science & Business Media
  • Release : 06 October 2009
GET THIS BOOKAdvances in Machine Learning

The First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2–4, 2009.This was the ?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related ?elds to share their new ideas and research ?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four

Independent Component Analysis

Independent Component Analysis
  • Author : Aapo Hyvärinen,Juha Karhunen,Erkki Oja
  • Publisher : John Wiley & Sons
  • Release : 05 April 2004
GET THIS BOOKIndependent Component Analysis

A comprehensive introduction to ICA for students andpractitioners Independent Component Analysis (ICA) is one of the most excitingnew topics in fields such as neural networks, advanced statistics,and signal processing. This is the first book to provide acomprehensive introduction to this new technique complete with thefundamental mathematical background needed to understand andutilize it. It offers a general overview of the basics of ICA,important solutions and algorithms, and in-depth coverage of newapplications in image processing, telecommunications, audio signalprocessing, and more.

Advances in E-Learning: Experiences and Methodologies

Advances in E-Learning: Experiences and Methodologies
  • Author : Garc¡a-Pe¤alvo, Francisco Jos‚
  • Publisher : IGI Global
  • Release : 31 March 2008
GET THIS BOOKAdvances in E-Learning: Experiences and Methodologies

Web-based training, known as e-learning, has experienced a great evolution and growth in recent years, as the capacity for education is no longer limited by physical and time constraints. The emergence of such a prized learning tool mandates a comprehensive evaluation of the effectiveness and implications of e-learning. Advances in E-Learning: Experiences and Methodologies explores the technical, pedagogical, methodological, tutorial, legal, and emotional aspects of e-learning, considering and analyzing its different application contexts, and providing researchers and practitioners with an

Statistical Learning and Pattern Analysis for Image and Video Processing

Statistical Learning and Pattern Analysis for Image and Video Processing
  • Author : Nanning Zheng,Jianru Xue
  • Publisher : Springer Science & Business Media
  • Release : 25 July 2009
GET THIS BOOKStatistical Learning and Pattern Analysis for Image and Video Processing

Why are We Writing This Book? Visual data (graphical, image, video, and visualized data) affect every aspect of modern society. The cheap collection, storage, and transmission of vast amounts of visual data have revolutionized the practice of science, technology, and business. Innovations from various disciplines have been developed and applied to the task of designing intelligent machines that can automatically detect and exploit useful regularities (patterns) in visual data. One such approach to machine intelligence is statistical learning and pattern

Neural Networks and Learning Machines

Neural Networks and Learning Machines
  • Author : Simon S. Haykin
  • Publisher : Prentice Hall
  • Release : 27 January 2021
GET THIS BOOKNeural Networks and Learning Machines

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised

Artificial Neural Networks and Machine Learning - ICANN 2011

Artificial Neural Networks and Machine Learning - ICANN 2011
  • Author : Timo Honkela,Włodzisław Duch,Mark Girolami,Samuel Kaski
  • Publisher : Springer Science & Business Media
  • Release : 14 June 2011
GET THIS BOOKArtificial Neural Networks and Machine Learning - ICANN 2011

This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.