New Directions In Statistical Signal Processing
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New Directions In Statistical Signal Processing
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Author : Simon S. Haykin
language : en
Publisher:
Release Date : 2007
New Directions In Statistical Signal Processing written by Simon S. Haykin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields. Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines.The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs).
Statistical Network Analysis Models Issues And New Directions
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Author : Edoardo M. Airoldi
language : en
Publisher: Springer
Release Date : 2008-04-12
Statistical Network Analysis Models Issues And New Directions written by Edoardo M. Airoldi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-12 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.
Computational Matter
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Author : Susan Stepney
language : en
Publisher: Springer
Release Date : 2018-07-20
Computational Matter written by Susan Stepney and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-20 with Computers categories.
This book is concerned with computing in materio: that is, unconventional computing performed by directly harnessing the physical properties of materials. It offers an overview of the field, covering four main areas of interest: theory, practice, applications and implications. Each chapter synthesizes current understanding by deliberately bringing together researchers across a collection of related research projects. The book is useful for graduate students, researchers in the field, and the general scientific reader who is interested in inherently interdisciplinary research at the intersections of computer science, biology, chemistry, physics, engineering and mathematics.
Developments In Natural Intelligence Research And Knowledge Engineering Advancing Applications
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Author : Wang, Yingxu
language : en
Publisher: IGI Global
Release Date : 2012-06-30
Developments In Natural Intelligence Research And Knowledge Engineering Advancing Applications written by Wang, Yingxu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-30 with Computers categories.
"This book covers the intricate worlds of thought, comprehension, intelligence, and knowledge through the scientific field of Cognitive Science, covering topics that have been pivotal at major conferences covering Cognitive Science"--Provided by publisher.
Machine Audition Principles Algorithms And Systems
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Author : Wang, Wenwu
language : en
Publisher: IGI Global
Release Date : 2010-07-31
Machine Audition Principles Algorithms And Systems written by Wang, Wenwu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-31 with Computers categories.
Machine audition is the study of algorithms and systems for the automatic analysis and understanding of sound by machine. It has recently attracted increasing interest within several research communities, such as signal processing, machine learning, auditory modeling, perception and cognition, psychology, pattern recognition, and artificial intelligence. However, the developments made so far are fragmented within these disciplines, lacking connections and incurring potentially overlapping research activities in this subject area. Machine Audition: Principles, Algorithms and Systems contains advances in algorithmic developments, theoretical frameworks, and experimental research findings. This book is useful for professionals who want an improved understanding about how to design algorithms for performing automatic analysis of audio signals, construct a computing system for understanding sound, and learn how to build advanced human-computer interactive systems.
New Directions In Statistical Signal Processing From Systems To Brains
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Author : Haykin Et Al. (eds.)
language : en
Publisher:
Release Date : 2007
New Directions In Statistical Signal Processing From Systems To Brains written by Haykin Et Al. (eds.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Neural computers categories.
Advances In Intelligent Signal Processing And Data Mining
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Author : Petia Georgieva
language : en
Publisher: Springer
Release Date : 2012-07-27
Advances In Intelligent Signal Processing And Data Mining written by Petia Georgieva and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-27 with Technology & Engineering categories.
The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.
Handbook On Computational Intelligence In 2 Volumes
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Author : Plamen Parvanov Angelov
language : en
Publisher: World Scientific
Release Date : 2016-03-18
Handbook On Computational Intelligence In 2 Volumes written by Plamen Parvanov Angelov and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-18 with Computers categories.
With the Internet, the proliferation of Big Data, and autonomous systems, mankind has entered into an era of 'digital obesity'. In this century, computational intelligence, such as thinking machines, have been brought forth to process complex human problems in a wide scope of areas — from social sciences, economics and biology, medicine and social networks, to cyber security.The Handbook of Computational Intelligence (in two volumes) prompts readers to look at these problems from a non-traditional angle. It takes a step by step approach, supported by case studies, to explore the issues that have arisen in the process. The Handbook covers many classic paradigms, as well as recent achievements and future promising developments to solve some of these very complex problems. Volume one explores the subjects of fuzzy logic and systems, artificial neural networks, and learning systems. Volume two delves into evolutionary computation, hybrid systems, as well as the applications of computational intelligence in decision making, the process industry, robotics, and autonomous systems.This work is a 'one-stop-shop' for beginners, as well as an inspirational source for more advanced researchers. It is a useful resource for lecturers and learners alike.
Dataset Shift In Machine Learning
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Author : Joaquin Quinonero-Candela
language : en
Publisher: MIT Press
Release Date : 2022-06-07
Dataset Shift In Machine Learning written by Joaquin Quinonero-Candela and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-07 with Computers categories.
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. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
Log Linear Models Extensions And Applications
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Author : Aleksandr Aravkin
language : en
Publisher: MIT Press
Release Date : 2024-12-03
Log Linear Models Extensions And Applications written by Aleksandr Aravkin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications. Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives. Contributors Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg