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"A First Course in Machine Learning by Simon Rogers and Mark
Girolami is the best introductory book for ML currently available.
It combines rigor and precision with accessibility, starts from a
detailed explanation of the basic foundations of Bayesian analysis
in the simplest of settings, and goes all the way to the frontiers
of the subject such as infinite mixture models, GPs, and MCMC."
-Devdatt Dubhashi, Professor, Department of Computer Science and
Engineering, Chalmers University, Sweden "This textbook manages to
be easier to read than other comparable books in the subject while
retaining all the rigorous treatment needed. The new chapters put
it at the forefront of the field by covering topics that have
become mainstream in machine learning over the last decade."
-Daniel Barbara, George Mason University, Fairfax, Virginia, USA
"The new edition of A First Course in Machine Learning by Rogers
and Girolami is an excellent introduction to the use of statistical
methods in machine learning. The book introduces concepts such as
mathematical modeling, inference, and prediction, providing 'just
in time' the essential background on linear algebra, calculus, and
probability theory that the reader needs to understand these
concepts." -Daniel Ortiz-Arroyo, Associate Professor, Aalborg
University Esbjerg, Denmark "I was impressed by how closely the
material aligns with the needs of an introductory course on machine
learning, which is its greatest strength...Overall, this is a
pragmatic and helpful book, which is well-aligned to the needs of
an introductory course and one that I will be looking at for my own
students in coming months." -David Clifton, University of Oxford,
UK "The first edition of this book was already an excellent
introductory text on machine learning for an advanced undergraduate
or taught masters level course, or indeed for anybody who wants to
learn about an interesting and important field of computer science.
The additional chapters of advanced material on Gaussian process,
MCMC and mixture modeling provide an ideal basis for practical
projects, without disturbing the very clear and readable exposition
of the basics contained in the first part of the book." -Gavin
Cawley, Senior Lecturer, School of Computing Sciences, University
of East Anglia, UK "This book could be used for junior/senior
undergraduate students or first-year graduate students, as well as
individuals who want to explore the field of machine learning...The
book introduces not only the concepts but the underlying ideas on
algorithm implementation from a critical thinking perspective."
-Guangzhi Qu, Oakland University, Rochester, Michigan, USA
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Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II (Paperback, Edition.)
Timo Honkela, Wlodzislaw Duch, Mark Girolami, Samuel Kaski
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R1,622
Discovery Miles 16 220
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Ships in 10 - 15 working days
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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.
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Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I (Paperback, Edition.)
Timo Honkela, Wlodzislaw Duch, Mark Girolami, Samuel Kaski
|
R1,595
Discovery Miles 15 950
|
Ships in 10 - 15 working days
|
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.
|
Pattern Recognition in Bioinformatics - 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009, Proceedings (Paperback, 2009 ed.)
Visakan Kadirkamanathan, Guido Sanguinetti, Mark Girolami, Mahesan Niranjan, Josselin Noirel
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R1,612
Discovery Miles 16 120
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Ships in 10 - 15 working days
|
The Pattern Recognition in Bioinformatics (PRIB) meeting was
established in 2006 under the auspices of the International
Association for Pattern Recognition (IAPR) to create a focus for
the development and application of pattern recognition techniques
in the biological domain. PRIB's aim to explore the full spectrum
of pattern recognition application was re?ected in the breadth of
techniquesrepresented in this year's subm- sions and in this book.
These range from image analysis for biomedical data to systems
biology. We
werefortunatetohaveinvitedspeakersofthehighestcalibredeliveringkeynotes
at the conference. These were Pierre Baldi (UC Irvine), Alvis
Brazma (EMBL-EBI), GunnarRats .. ch(MPITubi ..
ngen)andMichaelUnser(EPFL).Weacknowledgesupportof
theEUFP7NetworkofExcellencePASCAL2forpartiallyfundingtheinvitedspeakers.
Immediately prior to the conference, we hosted half day of tutorial
lectures, while a special session on "Machine Learningfor
IntegrativeGenomics" was held immediately after the main
conference.Duringthe conference,a poster session was heldwith
further discussion.
Wewouldlikeonceagaintothankalltheauthorsforthehighqualityofsubmissions,
as well as Yorkshire South and the University of Shef?eld for
providing logistical help in organizing the conference. Finally, we
would like to thank Springer for their help in assembling this
proceedings volume and for the continued support of PRIB.
This book constitutes the refereed proceedings of the 24th European Colloquium on Information Retrieval Research, ECIR 2002, held in Glasgow, UK, in March 2002.The 23 revised full papers presented were carefully reviewed and selected from a total of 52 submissions. The papers are organized in topical sections on multimedia, Web-information retrieval, query modification, soft computing, models, categorization, structured documents, cross-language issues, and interactive systems.
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.
This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas; noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualisation, data mining and biomedical data analysis. Self-Organising Neural Networks will be of interest to postgraduate students and researchers in Connectionist AI, Signal Processing and Neural Networks, research and development workers, and technology development engineers and research engineers.
"A First Course in Machine Learning by Simon Rogers and Mark
Girolami is the best introductory book for ML currently available.
It combines rigor and precision with accessibility, starts from a
detailed explanation of the basic foundations of Bayesian analysis
in the simplest of settings, and goes all the way to the frontiers
of the subject such as infinite mixture models, GPs, and MCMC."
-Devdatt Dubhashi, Professor, Department of Computer Science and
Engineering, Chalmers University, Sweden "This textbook manages to
be easier to read than other comparable books in the subject while
retaining all the rigorous treatment needed. The new chapters put
it at the forefront of the field by covering topics that have
become mainstream in machine learning over the last decade."
-Daniel Barbara, George Mason University, Fairfax, Virginia, USA
"The new edition of A First Course in Machine Learning by Rogers
and Girolami is an excellent introduction to the use of statistical
methods in machine learning. The book introduces concepts such as
mathematical modeling, inference, and prediction, providing 'just
in time' the essential background on linear algebra, calculus, and
probability theory that the reader needs to understand these
concepts." -Daniel Ortiz-Arroyo, Associate Professor, Aalborg
University Esbjerg, Denmark "I was impressed by how closely the
material aligns with the needs of an introductory course on machine
learning, which is its greatest strength...Overall, this is a
pragmatic and helpful book, which is well-aligned to the needs of
an introductory course and one that I will be looking at for my own
students in coming months." -David Clifton, University of Oxford,
UK "The first edition of this book was already an excellent
introductory text on machine learning for an advanced undergraduate
or taught masters level course, or indeed for anybody who wants to
learn about an interesting and important field of computer science.
The additional chapters of advanced material on Gaussian process,
MCMC and mixture modeling provide an ideal basis for practical
projects, without disturbing the very clear and readable exposition
of the basics contained in the first part of the book." -Gavin
Cawley, Senior Lecturer, School of Computing Sciences, University
of East Anglia, UK "This book could be used for junior/senior
undergraduate students or first-year graduate students, as well as
individuals who want to explore the field of machine learning...The
book introduces not only the concepts but the underlying ideas on
algorithm implementation from a critical thinking perspective."
-Guangzhi Qu, Oakland University, Rochester, Michigan, USA
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