Multiple Classifier Systems : Second International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001 Proceedings / edited by Josef Kittler, Fabio Roli.

Format:

Book

Contributor:

Kittler, Josef, 1946- editor.

Roli, Fabio, 1962- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science 0302-9743 ; 2096.

Lecture Notes in Computer Science, 0302-9743 ; 2096

Language:

English

Subjects (All):

Artificial intelligence.

Pattern perception.

Optical data processing.

Computers.

Algorithms.

Artificial Intelligence.

Pattern Recognition.

Image Processing and Computer Vision.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Local Subjects:

Artificial Intelligence.

Pattern Recognition.

Image Processing and Computer Vision.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Physical Description:

1 online resource (XII, 456 pages).

Edition:

First edition 2001.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.

System Details:

text file PDF

Summary:

Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.

Contents:

Bagging and Boosting

Bagging and the Random Subspace Method for Redundant Feature Spaces

Performance Degradation in Boosting

A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models

Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis

Learning Classification RBF Networks by Boosting

MCS Design Methodology

Data Complexity Analysis for Classifier Combination

Genetic Programming for Improved Receiver Operating Characteristics

Methods for Designing Multiple Classifier Systems

Decision-Level Fusion in Fingerprint Verification

Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition

Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'

Averaging Weak Classifiers

Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds

Ensemble Classifiers

Multiple Classifier Systems Based on Interpretable Linear Classifiers

Least Squares and Estimation Measures via Error Correcting Output Code

Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis

Information Analysis of Multiple Classifier Fusion?

Limiting the Number of Trees in Random Forests

Learning-Data Selection Mechanism through Neural Networks Ensemble

A Multi-SVM Classification System

Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System

Feature Spaces for MCS

Feature Weighted Ensemble Classifiers - A Modified Decision Scheme

Feature Subsets for Classifier Combination: An Enumerative Experiment

Input Decimation Ensembles: Decorrelation through Dimensionality Reduction

Classifier Combination as a Tomographic Process

MCS in Remote Sensing

A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps

Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances

Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data

Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers

One Class MCS and Clustering

Combining One-Class Classifiers

Finding Consistent Clusters in Data Partitions

A Self-Organising Approach to Multiple Classifier Fusion

Combination Strategies

Error Rejection in Linearly Combined Multiple Classifiers

Relationship of Sum and Vote Fusion Strategies

Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation

On Combining Dissimilarity Representations

Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System

Classification of Time Series Utilizing Temporal and Decision Fusion

Use of Positional Information in Sequence Alignment for Multiple Classifier Combination

Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting

Tree-Structured Support Vector Machines for Multi-class Pattern Recognition

On the Combination of Different Template Matching Strategies for Fast Face Detection

Improving Product by Moderating k-NN Classifiers

Automatic Model Selection in a Hybrid Perceptron/Radial Network.

Other Format:

Printed edition:

ISBN:

978-3-540-48219-2

9783540482192

Access Restriction:

Restricted for use by site license.