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This book honours the outstanding contributions of Vladimir Vapnik,
a rare example of a scientist for whom the following statements
hold true simultaneously: his work led to the inception of a new
field of research, the theory of statistical learning and empirical
inference; he has lived to see the field blossom; and he is still
as active as ever. He started analyzing learning algorithms in the
1960s and he invented the first version of the generalized portrait
algorithm. He later developed one of the most successful methods in
machine learning, the support vector machine (SVM) - more than just
an algorithm, this was a new approach to learning problems,
pioneering the use of functional analysis and convex optimization
in machine learning. Part I of this book contains three chapters
describing and witnessing some of Vladimir Vapnik's contributions
to science. In the first chapter, Leon Bottou discusses the seminal
paper published in 1968 by Vapnik and Chervonenkis that lay the
foundations of statistical learning theory, and the second chapter
is an English-language translation of that original paper. In the
third chapter, Alexey Chervonenkis presents a first-hand account of
the early history of SVMs and valuable insights into the first
steps in the development of the SVM in the framework of the
generalised portrait method. The remaining chapters, by leading
scientists in domains such as statistics, theoretical computer
science, and mathematics, address substantial topics in the theory
and practice of statistical learning theory, including SVMs and
other kernel-based methods, boosting, PAC-Bayesian theory, online
and transductive learning, loss functions, learnable function
classes, notions of complexity for function classes, multitask
learning, and hypothesis selection. These contributions include
historical and context notes, short surveys, and comments on future
research directions. This book will be of interest to researchers,
engineers, and graduate students engaged with all aspects of
statistical learning.
This book honours the outstanding contributions of Vladimir Vapnik,
a rare example of a scientist for whom the following statements
hold true simultaneously: his work led to the inception of a new
field of research, the theory of statistical learning and empirical
inference; he has lived to see the field blossom; and he is still
as active as ever. He started analyzing learning algorithms in the
1960s and he invented the first version of the generalized portrait
algorithm. He later developed one of the most successful methods in
machine learning, the support vector machine (SVM) - more than just
an algorithm, this was a new approach to learning problems,
pioneering the use of functional analysis and convex optimization
in machine learning. Part I of this book contains three chapters
describing and witnessing some of Vladimir Vapnik's contributions
to science. In the first chapter, Leon Bottou discusses the seminal
paper published in 1968 by Vapnik and Chervonenkis that lay the
foundations of statistical learning theory, and the second chapter
is an English-language translation of that original paper. In the
third chapter, Alexey Chervonenkis presents a first-hand account of
the early history of SVMs and valuable insights into the first
steps in the development of the SVM in the framework of the
generalised portrait method. The remaining chapters, by leading
scientists in domains such as statistics, theoretical computer
science, and mathematics, address substantial topics in the theory
and practice of statistical learning theory, including SVMs and
other kernel-based methods, boosting, PAC-Bayesian theory, online
and transductive learning, loss functions, learnable function
classes, notions of complexity for function classes, multitask
learning, and hypothesis selection. These contributions include
historical and context notes, short surveys, and comments on future
research directions. This book will be of interest to researchers,
engineers, and graduate students engaged with all aspects of
statistical learning.
We are delighted to present the proceedings of DAGM 2004, and wish
to - press our gratitude to the many people whose e?orts made the
success of the conference possible. We received 146 contributions
of which we were able to - cept 22 as oral presentations and 48 as
posters. Each paper received 3 reviews, upon which decisions were
based. We are grateful for the dedicated work of the 38 members of
the program committee and the numerous referees. The careful review
process led to the exciting program which we are able to present in
this volume. Among the highlights of the meeting were the talks of
our four invited spe- ers, renowned experts in areas spanning
learning in theory, in vision and in robotics: - William T.
Freeman, Arti?cial Intelligence Laboratory, MIT: Sharing F- tures
for Multi-class Object Detection -
PietroPerona,Caltech:TowardsUnsupervisedLearningofObjectCategories
- StefanSchaal,DepartmentofComputerScience,UniversityofSouthernC-
ifornia: Real-Time Statistical Learning for Humanoid Robotics -
Vladimir Vapnik, NEC Research Institute: Empirical Inference
WearegratefulforeconomicsupportfromHondaResearchInstituteEurope,
ABW GmbH, Transtec AG, DaimlerChrysler, and Stemmer Imaging GmbH,
which enabled us to ? nance best paper prizes and a limited number
of travel grants. Many thanks to our local support Sabrina
Nielebock and Dagmar Maier, who dealt with the unimaginably diverse
range of practical tasks involved in planning a DAGM symposium.
Thanks to Richard van de Stadt for providing excellent software and
support for handling the reviewing process. A special thanks goes
to Jeremy Hill, who wrote and maintained the conference website.
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Learning Theory and Kernel Machines - 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings (Paperback, 2003 ed.)
Bernhard Schoelkopf, Manfred K Warmuth
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R3,402
Discovery Miles 34 020
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Ships in 10 - 15 working days
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This volume contains papers presented at the joint 16th Annual
Conference on Learning Theory (COLT) and the 7th Annual Workshop on
Kernel Machines, heldinWashington, DC, USA, duringAugust24
27,2003.COLT, whichrecently merged with EuroCOLT, has traditionally
been a meeting place for learning theorists. We hope that COLT will
bene't from the collocation with the annual
workshoponkernelmachines,
formerlyheldasaNIPSpostconferenceworkshop. The technical program
contained 47 papers selected from 92 submissions. All
47paperswerepresentedasposters;22ofthepaperswereadditionallypresented
astalks.Therewerealsotwotargetareaswithinvitedcontributions.Incompu-
tional game theory, atutorialentitled
LearningTopicsinGame-TheoreticDe- sionMaking
wasgivenbyMichaelLittman, andaninvitedpaperon AGeneral Class of
No-Regret Learning Algorithms and Game-Theoretic Equilibria was
contributed by Amy Greenwald. In natural language processing, a
tutorial on Machine Learning Methods in Natural Language Processing
was presented by Michael Collins, followed by two invited talks,
Learning from Uncertain Data by Mehryar Mohri and Learning and
Parsing Stochastic Uni?cation- Based Grammars by Mark Johnson. In
addition to the accepted papers and invited presentations, we
solicited short open problems that were reviewed and included in
the proceedings. We hope that reviewed open problems might become a
new tradition for COLT. Our goal was to select simple signature
problems whose solutions are likely to inspire further research.
For some of the problems the authors o?ered monetary rewards. Yoav
Freund acted as the open problem area chair. The open problems were
presented as posters at the conference."
Numerous fascinating breakthroughs in biotechnology have generated
large volumes and diverse types of high throughput data that demand
the development of efficient and appropriate tools in computational
statistics integrated with biological knowledge and computational
algorithms. This volume collects contributed chapters from leading
researchers to survey the many active research topics and promote
the visibility of this research area. This volume is intended to
provide an introductory and reference book for students and
researchers who are interested in the recent developments of
computational statistics in computational biology.
Now in its second edition, this handbook collects authoritative
contributions on modern methods and tools in statistical
bioinformatics with a focus on the interface between computational
statistics and cutting-edge developments in computational biology.
The three parts of the book cover statistical methods for
single-cell analysis, network analysis, and systems biology, with
contributions by leading experts addressing key topics in
probabilistic and statistical modeling and the analysis of massive
data sets generated by modern biotechnology. This handbook will
serve as a useful reference source for students, researchers and
practitioners in statistics, computer science and biological and
biomedical research, who are interested in the latest developments
in computational statistics as applied to computational biology.
Numerous fascinating breakthroughs in biotechnology have generated
large volumes and diverse types of high throughput data that demand
the development of efficient and appropriate tools in computational
statistics integrated with biological knowledge and computational
algorithms. This volume collects contributed chapters from leading
researchers to survey the many active research topics and promote
the visibility of this research area. This volume is intended to
provide an introductory and reference book for students and
researchers who are interested in the recent developments of
computational statistics in computational biology.
A concise and self-contained introduction to causal inference,
increasingly important in data science and machine learning. The
mathematization of causality is a relatively recent development,
and has become increasingly important in data science and machine
learning. This book offers a self-contained and concise
introduction to causal models and how to learn them from data.
After explaining the need for causal models and discussing some of
the principles underlying causal inference, the book teaches
readers how to use causal models: how to compute intervention
distributions, how to infer causal models from observational and
interventional data, and how causal ideas could be exploited for
classical machine learning problems. All of these topics are
discussed first in terms of two variables and then in the more
general multivariate case. The bivariate case turns out to be a
particularly hard problem for causal learning because there are no
conditional independences as used by classical methods for solving
multivariate cases. The authors consider analyzing statistical
asymmetries between cause and effect to be highly instructive, and
they report on their decade of intensive research into this
problem. The book is accessible to readers with a background in
machine learning or statistics, and can be used in graduate courses
or as a reference for researchers. The text includes code snippets
that can be copied and pasted, exercises, and an appendix with a
summary of the most important technical concepts.
State-of-the-art algorithms and theory in a novel domain of machine
learning, prediction when the output has structure. Machine
learning develops intelligent computer systems that are able to
generalize from previously seen examples. A new domain of machine
learning, in which the prediction must satisfy the additional
constraints found in structured data, poses one of machine
learning's greatest challenges: learning functional dependencies
between arbitrary input and output domains. This volume presents
and analyzes the state of the art in machine learning algorithms
and theory in this novel field. The contributors discuss
applications as diverse as machine translation, document markup,
computational biology, and information extraction, among others,
providing a timely overview of an exciting field. Contributors
Yasemin Altun, Goekhan Bakir, Olivier Bousquet, Sumit Chopra,
Corinna Cortes, Hal Daume III, Ofer Dekel, Zoubin Ghahramani, Raia
Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann,
Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford
Noble, Fernando Perez-Cruz, Massimiliano Pontil, Marc'Aurelio
Ranzato, Juho Rousu, Craig Saunders, Bernhard Schoelkopf, Matthias
W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer,
Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis
Tsochantaridis, S.V.N Vishwanathan, Jason Weston
A comprehensive introduction to Support Vector Machines and related
kernel methods. In the 1990s, a new type of learning algorithm was
developed, based on results from statistical learning theory: the
Support Vector Machine (SVM). This gave rise to a new class of
theoretically elegant learning machines that use a central concept
of SVMs--kernels-for a number of learning tasks. Kernel machines
provide a modular framework that can be adapted to different tasks
and domains by the choice of the kernel function and the base
algorithm. They are replacing neural networks in a variety of
fields, including engineering, information retrieval, and
bioinformatics. Learning with Kernels provides an introduction to
SVMs and related kernel methods. Although the book begins with the
basics, it also includes the latest research. It provides all of
the concepts necessary to enable a reader equipped with some basic
mathematical knowledge to enter the world of machine learning using
theoretically well-founded yet easy-to-use kernel algorithms and to
understand and apply the powerful algorithms that have been
developed over the last few years.
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