Series: Computational Imaging and Vision, Vol. 29
2005, XV, 242 p., Hardcover
ISBN: 1-4020-3274-9
About this book
The goal of this book is to address the use of several important
machine learning techniques into computer vision applications. An
innovative combination of computer vision and machine learning
techniques has the promise of advancing the field of computer
vision, which contributes to better understanding of complex real-world
applications. The effective usage of machine learning technology
in real-world computer vision problems requires understanding the
domain of application, abstraction of a learning problem from a
given computer vision task, and the selection of appropriate
representations for the learnable (input) and learned (internal)
entities of the system.
In this book, we address all these important aspects from a new
perspective: that the key element in the current computer
revolution is the use of machine learning to capture the
variations in visual appearance, rather than having the designer
of the model accomplish this. As a bonus, models learned from
large datasets are likely to be more robust and more realistic
than the brittle all-design models.
Table of contents
Foreword. Preface
1. INTRODUCTION. 1 Research Issues on Learning in Computer Vision.
2 Overview of the Book. 3 Contributions.
2. THEORY: PROBABILISTIC CLASSIFIERS. 1 Introduction. 2
Preliminaries and Notations. 3 Bayes Optimal Error and Entropy. 4
Analysis of Classification Error of Estimated (Mismatched)Distribution.
5 Density of Distributions. 6 Complex Probabilistic Models and
Small Sample Effects. 7 Summary.
3. THEORY: GENERALIZATION BOUNDS. 1 Introduction. 2 Preliminaries.
3 A Margin Distribution Based Bound. 4 Analysis. 5 Summary.
4. THEORY: SEMI-SUPERVISED LEARNING. 1 Introduction.2 Properties
of Classification. 3 Existing Literature. 4 Semi-supervised
Learning Using Maximum Likelihood Estimation. 5 Asymptotic
Properties of Maximum Likelihood Estimation with Labeled and
Unlabeled Data. 6 Learning with Finite Data. 7 Concluding Remarks.
5. ALGORITHM: MAXIMUM LIKELIHOOD MINIMUM ENTROPY HMM. 1 Previous
Work. 2 Mutual Information, Bayes Optimal Error, Entropy, and
Conditional Probability. 3 Maximum Mutual Information HMMs. 4
Discussion. 5 Experimental Results. 6 Summary.
6. ALGORITHM: MARGIN DISTRIBUTION OPTIMIZATION. 1 Introduction. 2
A Margin Distribution Based Bound. 3 Existing Learning Algorithms.
4 The Margin Distribution Optimization (MDO) Algorithm. 5
Experimental Evaluation. 6 Conclusions. 7. ALGORITHM: LEARNING
THE STRUCTURE OF BAYESIAN NETWORK CLASSIFIERS. 1 Introduction. 2
Bayesian Network Classifiers. 3 Switching between Models: Naive
Bayes and TAN Classifiers. 4 Learning the Structure of Bayesian
Network Classifiers: Existing Approaches. 5 Classification Driven
Stochastic Structure Search. 6 Experiments. 7 Should Unlabeled
Data Be Weighed Differently? 8 Active Learning. 9 Concluding
Remarks.
8. APPLICATION: OFFICE ACTIVITY RECOGNITION. 1 Context-Sensitive
Systems. 2 Towards Tractable and Robust Context Sensing. 3
Layered Hidden Markov Models (LHMMs). 4 Implementation of SEER. 5
Experiments. 6 Related Representations. 7 Summary.
9. APPLICATION: MULTIMODAL EVENT DETECTION. 1 Fusion Models: A
Review. 2 A Hierarchical Fusion Model. 3 Experimental Setup,
Features, and Results. 4 Summary.
10. APPLICATION: FACIAL EXPRESSION RECOGNITION. 1 Introduction. 2
Human Emotion Research. 3 Facial Expression Recognition System. 4
Experimental Analysis. 5 Discussion.
11. APPLICATION: BAYESIAN NETWORK CLASSIFIERS FOR FACE DETECTION.
1 Introduction. 2 Related Work. 3 Applying Bayesian Network
Classifiers to Face Detection. 4 Experiments. 5 Discussion.
References. Index.
2005, XXII, 538 p. 115 illus., Hardcover
ISBN: 0-387-20473-3
About this textbook
A comprehensive survey of computer network security concepts,
methods, and practices. This authoritative volume provides an
optimal description of the principles and applications of
computer network security in particular, and cyberspace security
in general. The book is thematically divided into three segments:
Part I describes the operation and security conditions
surrounding computer networks; Part II builds from there and
exposes readers to the prevailing security situation based on a
constant security threat; and Part III - the core - presents
readers with most of the best practices and solutions currently
in use. It is intended as both a teaching tool and reference.
This broad-ranging text/reference comprehensively surveys
computer network security concepts, methods, and practices and
covers network security tools, policies, and administrative goals
in an integrated manner. It is an essential security resource for
undergraduate or graduate study, practitioners in networks, and
professionals who develop and maintain secure computer network
systems.
Table of contents
Preface.- Part I : Understanding Computer Network Security.-
Computer Network Fundamentals.- Understanding Network Security.-
Part II: Security Challenges to Computer Networks.- Security
Threats to Computer Networks.- Computer Network Vulnerabilities.-
Cyber Crimes and Hackers.- Hostile Scripts.- Security Assessment,
Analysis, and Assurance.- Part III: Dealing with Network Security
Challenges.- Access Control and Authorization.-Authentication.-
Cryptography.- Firewalls.- System Intrusion Detection and
Prevention.- Computer and Network Forensics.- Virus and Content
Filtering.- Security Evaluations of Computer Products.- Computer
Network Security Protocols and Standards.- Security in Wireless
Networks and Devices.- Other Efforts to Secure Information and
Computer Networks.- Looking Ahead ? Security Beyond Computer
Networks.- Part IV: Projects.- Projects.- Index.
Joseph Auslander
A group theoretic condition in topological dynamics
Valera Berestovskii and Conrad Plaut
The universal cover of the quotient of a locally defined group
A. Bouziad and J.-P. Troallic
Nonseparability and uniformities in topological groups
Timothy J. Carlson, Neil Hindman and Dona Strauss
The Graham-Rothschild Theorem and the algebra of \betaW
W. W. Comfort
Tampering with pseudocompact groups
W.W. Comfort and A.W. Hager
Maximal realcompact (and other) topologies
Szymon Dolecki
Elimination of covers in completeness
Jorge Galindo
Totally bounded group topologies that are Bohr topologies of LCA
groups
Helge Glockner
Examples of differentiable mappings into non-locally convex
spaces
Ivan Gotchev and Hristo Minchev
On sequential properties of Noetherian topological spaces
H. Hattab and E. Salhi
Groups of homeomorphisms and spectral topology
Salvador Hernandez
Extension of continuous functions on product spaces, Bohr
compactification and almost periodic functions
Karl H. Hofmann and Sindey A. Morris
Lie theory and the structure of pro-Lie groups and pro-Lie
algebras
Gerald Itzkowitz
Functional balance, discrete balance, and balance in topological
groups
A. V. Karasev
The Urysohn identity for closed subsets of some nonmetrizable
manifolds
Hisao Kato
Topological entropy of monotone maps and confluent maps on
regular curves
Sabine Koppelberg
The Hales-Jewett theorem via retractions
Wieslaw Kubis and Arkady Leiderman
Semi-Eberlein spaces
Randall McCutcheon
Rhythmic functions and IP recurrence
Hector Mendez-Lango
The process of finding f' for an entire function f has infinite
topological entropy
W.H. Previts and T.S. Wu
Notes on tidy subgroups of locally compact totally disconnected
groups
Aleksandar Stojmirovic
Quasi-metric spaces with measure
Hideki Tsuiki
Dyadic subbases and efficiency properties of the induced {0, 1,
^}w-representations
Jan van Mill
A note on Ford's example
BiBTeX file of bibliographic information for this issue.
July 2005
ISBN 0-262-19534-8
7 x 10, 1696 pp.
The annual Neural Information Processing Systems (NIPS)
conference is the flagship meeting on neural computation. It
draws a diverse group of attendees -- physicists,
neuroscientists, mathematicians, statisticians, and computer
scientists. The presentations are interdisciplinary, with
contributions in algorithms, learning theory, cognitive science,
neuroscience, brain imaging, vision, speech and signal
processing, reinforcement learning and control, emerging
technologies, and applications. Only twenty-five percent of the
papers submitted are accepted for presentation at NIPS, so the
quality is exceptionally high. This volume contains the papers
presented at the December, 2004 conference, held in Vancouver.
Lawrence K. Saul is Assistant Professor in the Department of
Computer and Information Science at the University of
Pennsylvania and General Chair of the 2004 NIPS conference.
Yair Weiss is Senior Lecturer in the School of Computer Science
and Engineering at The Hebrew University of Jerusalem and Program
Chair of the 2004 NIPS conference.
Leon Bottou is Senior Research Scientist at NEC Laboratories
America in Princeton, New Jersey, and Publications Chair of the
2004 NIPS conference.
Series: Lecture Notes in Computational Science and Engineering, Vol. 46
2005, XIV, 258 p. 32 illus., Softcover
ISBN: 3-540-24546-4
About this book
This book is about computing eigenvalues, eigenvectors and invariant subspaces of matrices. The treatment includes generalized and structured eigenvalue problems, such as Hamiltonian or product eigenvalue problems. All vital aspects of eigenvalue computations are covered: theory, perturbation analysis, algorithms, high performance methodologies and software. The reader will learn about recently developed techniques which substantially improve the performance of some of the most widely numerical methods, the QR and the QZ algorithm as well as Krylov subspace methods. A unique feature of this book is the detailed treatment of structured eigenvalue problems, providing insight on accuracy and efficiency gains to be expected from algorithms that take the structure of a matrix into account.
Written for:
Computational scientists
Keywords:
computational methods
eigenvalue
matrix product
structured matrix