Multiple Classifier Systems de Josef Kittler

Multiple Classifier Systems

Josef Kittler

Fabio Roli

en Limba Engleză Paperback – 14 iun 2000

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.

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Paperback (3)	324^.82 lei 6-8 săpt.
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Springer – 20 iun 2001	331^.16 lei 6-8 săpt.

Preț

Express

Paperback (3)

324^.82 lei 6-8 săpt.

Springer – 12 iun 2002

324^.82 lei 6-8 săpt.

Springer – 14 iun 2000

328^.49 lei 6-8 săpt.

Springer – 20 iun 2001

331^.16 lei 6-8 săpt.

Cuprins

Ensemble Methods in Machine Learning.- Experiments with Classifier Combining Rules.- The “Test and Select” Approach to Ensemble Combination.- A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR.- Multiple Classifier Combination Methodologies for Different Output Levels.- A Mathematically Rigorous Foundation for Supervised Learning.- Classifier Combinations: Implementations and Theoretical Issues.- Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification.- Complexity of Classification Problems and Comparative Advantages of Combined Classifiers.- Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems.- Combining Fisher Linear Discriminants for Dissimilarity Representations.- A Learning Method of Feature Selection for Rough Classification.- Analysis of a Fusion Method for Combining Marginal Classifiers.- A hybrid projection based and radial basis function architecture.- Combining Multiple Classifiers in Probabilistic Neural Networks.- Supervised Classifier Combination through Generalized Additive Multi-model.- Dynamic Classifier Selection.- Boosting in Linear Discriminant Analysis.- Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination.- Applying Boosting to Similarity Literals for Time Series Classification.- Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS.- A New Evaluation Method for Expert Combination in Multi-expert System Designing.- Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems.- Self-Organizing Decomposition of Functions.- Classifier Instability and Partitioning.- A Hierarchical Multiclassifier System for Hyperspectral Data Analysis.-Consensus Based Classification of Multisource Remote Sensing Data.- Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps.- A Multiple Self-Organizing Map Scheme for Remote Sensing Classification.- Use of Lexicon Density in Evaluating Word Recognizers.- A Multi-expert System for Dynamic Signature Verification.- A Cascaded Multiple Expert System for Verification.- Architecture for Classifier Combination Using Entropy Measures.- Combining Fingerprint Classifiers.- Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework.- A Modular Neuro-Fuzzy Network for Musical Instruments Classification.- Classifier Combination for Grammar-Guided Sentence Recognition.- Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers.