# Advances in Independent Component Analysis and Learning Machines

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## Advances in Independent Component Analysis and Learning Machines

Author | : Ella Bingham,Samuel Kaski,Jorma Laaksonen,Jouko Lampinen |

Publsiher | : Academic Press |

Total Pages | : 328 |

Release | : 2015-05-14 |

ISBN 10 | : 0128028076 |

ISBN 13 | : 9780128028070 |

Language | : EN, FR, DE, ES & NL |

**Advances in Independent Component Analysis and Learning Machines Book Review:**

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.

## Advances in Independent Component Analysis

Author | : Mark Girolami |

Publsiher | : Springer Science & Business Media |

Total Pages | : 284 |

Release | : 2000-07-17 |

ISBN 10 | : 9781852332631 |

ISBN 13 | : 1852332638 |

Language | : EN, FR, DE, ES & NL |

**Advances in Independent Component Analysis Book Review:**

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 very first time. Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

## Recent Advances in Big Data and Deep Learning

Author | : Luca Oneto,Nicolò Navarin,Alessandro Sperduti,Davide Anguita |

Publsiher | : Springer |

Total Pages | : 392 |

Release | : 2019-04-02 |

ISBN 10 | : 3030168417 |

ISBN 13 | : 9783030168414 |

Language | : EN, FR, DE, ES & NL |

**Recent Advances in Big Data and Deep Learning Book Review:**

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 addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.

## Source Separation and Machine Learning

Author | : Jen-Tzung Chien |

Publsiher | : Academic Press |

Total Pages | : 384 |

Release | : 2018-11-01 |

ISBN 10 | : 0128045779 |

ISBN 13 | : 9780128045770 |

Language | : EN, FR, DE, ES & NL |

**Source Separation and Machine Learning Book Review:**

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 deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems

## Independent Component Analysis

Author | : Aapo Hyvärinen,Juha Karhunen,Erkki Oja |

Publsiher | : John Wiley & Sons |

Total Pages | : 504 |

Release | : 2004-04-05 |

ISBN 10 | : 0471464198 |

ISBN 13 | : 9780471464198 |

Language | : EN, FR, DE, ES & NL |

**Independent Component Analysis Book Review:**

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. Independent Component Analysis is divided into four sections thatcover: * General mathematical concepts utilized in the book * The basic ICA model and its solution * Various extensions of the basic ICA model * Real-world applications for ICA models Authors Hyvarinen, Karhunen, and Oja are well known for theircontributions to the development of ICA and here cover all therelevant theory, new algorithms, and applications in variousfields. Researchers, students, and practitioners from a variety ofdisciplines will find this accessible volume both helpful andinformative.

## Blind Source Separation

Author | : Ganesh R. Naik,Wenwu Wang |

Publsiher | : Springer |

Total Pages | : 551 |

Release | : 2014-05-21 |

ISBN 10 | : 3642550169 |

ISBN 13 | : 9783642550164 |

Language | : EN, FR, DE, ES & NL |

**Blind Source Separation Book Review:**

Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.

## Advances in Intelligent Data Analysis

Author | : Anonim |

Publsiher | : Unknown |

Total Pages | : 329 |

Release | : 2003 |

ISBN 10 | : |

ISBN 13 | : UOM:39015047962322 |

Language | : EN, FR, DE, ES & NL |

**Advances in Intelligent Data Analysis Book Review:**

## Advances in Machine Learning and Signal Processing

Author | : Ping Jack Soh,Wai Lok Woo,Hamzah Asyrani Sulaiman,Mohd Azlishah Othman,Mohd Shakir Saat |

Publsiher | : Springer |

Total Pages | : 312 |

Release | : 2016-06-18 |

ISBN 10 | : 3319322133 |

ISBN 13 | : 9783319322131 |

Language | : EN, FR, DE, ES & NL |

**Advances in Machine Learning and Signal Processing Book Review:**

This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learning and signal processing for engineering problems.

## Advances in Neural Information Processing Systems 13

Author | : Todd K. Leen,Thomas G. Dietterich,Volker Tresp |

Publsiher | : MIT Press |

Total Pages | : 1106 |

Release | : 2001 |

ISBN 10 | : 9780262122412 |

ISBN 13 | : 0262122413 |

Language | : EN, FR, DE, ES & NL |

**Advances in Neural Information Processing Systems 13 Book Review:**

The annual conference on Neural Information Processing Systems (NIPS) is the flagshipconference on neural computation. The conference is interdisciplinary, with contributions inalgorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing,reinforcement learning and control, implementations, and diverse applications. Only about 30 percentof the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high.These proceedings contain all of the papers that were presented at the 2000 conference.

## Neural Networks and Statistical Learning

Author | : Ke-Lin Du,M. N. S. Swamy |

Publsiher | : Springer Nature |

Total Pages | : 988 |

Release | : 2019-09-12 |

ISBN 10 | : 1447174526 |

ISBN 13 | : 9781447174523 |

Language | : EN, FR, DE, ES & NL |

**Neural Networks and Statistical Learning Book Review:**

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

## Central Auditory Processing and Neural Modeling

Author | : Paul F. Poon,John F. Brugge |

Publsiher | : Springer Science & Business Media |

Total Pages | : 282 |

Release | : 2012-12-06 |

ISBN 10 | : 1461553512 |

ISBN 13 | : 9781461553519 |

Language | : EN, FR, DE, ES & NL |

**Central Auditory Processing and Neural Modeling Book Review:**

The full power of combining experiment and theory has yet to be unleashed on studies of the neural mechanisms in the brain involved in acoustic information processing. In recent years, enormous amounts of physiological data have been generated in many laboratories around the world, characterizing electrical responses of neurons to a wide array of acoustic stimuli at all levels of the auditory neuroaxis. Modern approaches of cellular and molecular biology are leading to new understandings of synaptic transmission of acoustic information, while application of modern neuro-anatomical methods is giving us a fairly comprehensive view ofthe bewildering complexity of neural circuitry within and between the major nuclei of the central auditory pathways. Although there is still the need to gather more data at all levels of organization, a ma jor challenge in auditory neuroscience is to develop new frameworks within which existing and future data can be incorporated and unified, and which will guide future laboratory ex perimentation. Here the field can benefit greatly from neural modeling, which in the central auditory system is still in its infancy. Indeed, such an approach is essential if we are to address questions related to perception of complex sounds including human speech, to the many di mensions of spatial hearing, and to the mechanisms that underlie complex acoustico-motor behaviors.

## Advances in Financial Machine Learning

Author | : Marcos Lopez de Prado |

Publsiher | : John Wiley & Sons |

Total Pages | : 400 |

Release | : 2018-01-23 |

ISBN 10 | : 1119482119 |

ISBN 13 | : 9781119482116 |

Language | : EN, FR, DE, ES & NL |

**Advances in Financial Machine Learning Book Review:**

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

## The IEEE 2000 Adaptive Systems for Signal Processing Communications and Control Symposium

Author | : Anonim |

Publsiher | : Institute of Electrical & Electronics Engineers(IEEE) |

Total Pages | : 480 |

Release | : 2000 |

ISBN 10 | : |

ISBN 13 | : UOM:39015054417996 |

Language | : EN, FR, DE, ES & NL |

**The IEEE 2000 Adaptive Systems for Signal Processing Communications and Control Symposium Book Review:**

The proceedings of the Symposium on Adaptive Systems for Signal Processing, Communications, and Control, 2000. It addresses fundamentals of adaptive and learning systems; signal processing; radar/sonar; wireless communications; pattern recognition; chaos; and more.

## Journal of Machine Learning Research

Author | : Anonim |

Publsiher | : Unknown |

Total Pages | : 329 |

Release | : 2007 |

ISBN 10 | : |

ISBN 13 | : UCSD:31822036045433 |

Language | : EN, FR, DE, ES & NL |

**Journal of Machine Learning Research Book Review:**

## Learning Blind Source Separation

Author | : Francis René Bach |

Publsiher | : Unknown |

Total Pages | : 386 |

Release | : 2005 |

ISBN 10 | : |

ISBN 13 | : UCAL:C3500963 |

Language | : EN, FR, DE, ES & NL |

**Learning Blind Source Separation Book Review:**

## Machine Learning for Audio Image and Video Analysis

Author | : Francesco Camastra,Alessandro Vinciarelli |

Publsiher | : Springer |

Total Pages | : 561 |

Release | : 2015-07-21 |

ISBN 10 | : 144716735X |

ISBN 13 | : 9781447167358 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning for Audio Image and Video Analysis Book Review:**

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

## Advances in Neural Information Processing Systems 15

Author | : Suzanna Becker,Sebastian Thrun,Klaus Obermayer |

Publsiher | : MIT Press |

Total Pages | : 1687 |

Release | : 2003 |

ISBN 10 | : 9780262025508 |

ISBN 13 | : 0262025507 |

Language | : EN, FR, DE, ES & NL |

**Advances in Neural Information Processing Systems 15 Book Review:**

Proceedings of the 2002 Neural Information Processing Systems Conference. The annual Neural Information Processing (NIPS) meeting is the flagship conference on neural computation. The conference draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--and the presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and applications. Only about thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2002 conference.

## Neural Networks for Signal Processing VII

Author | : Jose Principe |

Publsiher | : Unknown |

Total Pages | : 667 |

Release | : 1997 |

ISBN 10 | : 9780780342576 |

ISBN 13 | : 0780342577 |

Language | : EN, FR, DE, ES & NL |

**Neural Networks for Signal Processing VII Book Review:**

## Neural Networks and Learning Machines

Author | : Simon S. Haykin |

Publsiher | : Prentice Hall |

Total Pages | : 906 |

Release | : 2009 |

ISBN 10 | : 0131471392 |

ISBN 13 | : 9780131471399 |

Language | : EN, FR, DE, ES & NL |

**Neural Networks and Learning Machines Book Review:**

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 and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

## Audio Source Separation and Speech Enhancement

Author | : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot |

Publsiher | : John Wiley & Sons |

Total Pages | : 504 |

Release | : 2018-10-22 |

ISBN 10 | : 1119279895 |

ISBN 13 | : 9781119279891 |

Language | : EN, FR, DE, ES & NL |

**Audio Source Separation and Speech Enhancement Book Review:**

Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting with sensor array processing, computational auditory scene analysis, and machine learning based approaches such as independent component analysis, respectively. This book is the first one to provide a comprehensive overview by presenting the common foundations and the differences between these techniques in a unified setting. Key features: Consolidated perspective on audio source separation and speech enhancement. Both historical perspective and latest advances in the field, e.g. deep neural networks. Diverse disciplines: array processing, machine learning, and statistical signal processing. Covers the most important techniques for both single-channel and multichannel processing. This book provides both introductory and advanced material suitable for people with basic knowledge of signal processing and machine learning. Thanks to its comprehensiveness, it will help students select a promising research track, researchers leverage the acquired cross-domain knowledge to design improved techniques, and engineers and developers choose the right technology for their target application scenario. It will also be useful for practitioners from other fields (e.g., acoustics, multimedia, phonetics, and musicology) willing to exploit audio source separation or speech enhancement as pre-processing tools for their own needs.