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121227s2004 gw | s |||| 0|eng d |
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|a 9783540301158
|9 978-3-540-30115-8
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|a 10.1007/b100702
|2 doi
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|a Q334-342
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|a Machine Learning: ECML 2004
|h [electronic resource] :
|b 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings /
|c edited by Jean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti, Dino Pedreschi.
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|a 1st ed. 2004.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2004.
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|a XVIII, 582 p.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Artificial Intelligence ;
|v 3201
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|a Invited Papers -- Random Matrices in Data Analysis -- Data Privacy -- Breaking Through the Syntax Barrier: Searching with Entities and Relations -- Real-World Learning with Markov Logic Networks -- Strength in Diversity: The Advance of Data Analysis -- Contributed Papers -- Filtered Reinforcement Learning -- Applying Support Vector Machines to Imbalanced Datasets -- Sensitivity Analysis of the Result in Binary Decision Trees -- A Boosting Approach to Multiple Instance Learning -- An Experimental Study of Different Approaches to Reinforcement Learning in Common Interest Stochastic Games -- Learning from Message Pairs for Automatic Email Answering -- Concept Formation in Expressive Description Logics -- Multi-level Boundary Classification for Information Extraction -- An Analysis of Stopping and Filtering Criteria for Rule Learning -- Adaptive Online Time Allocation to Search Algorithms -- Model Approximation for HEXQ Hierarchical Reinforcement Learning -- Iterative Ensemble Classification for Relational Data: A Case Study of Semantic Web Services -- Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics -- Experiments in Value Function Approximation with Sparse Support Vector Regression -- Constructive Induction for Classifying Time Series -- Fisher Kernels for Logical Sequences -- The Enron Corpus: A New Dataset for Email Classification Research -- Margin Maximizing Discriminant Analysis -- Multi-objective Classification with Info-Fuzzy Networks -- Improving Progressive Sampling via Meta-learning on Learning Curves -- Methods for Rule Conflict Resolution -- An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk -- Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection -- Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework -- Justification-Based Selection of Training Examples for Case Base Reduction -- Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers -- Feature Selection Filters Based on the Permutation Test -- Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning -- Improving Random Forests -- The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering -- Using String Kernels to Identify Famous Performers from Their Playing Style -- Associative Clustering -- Learning to Fly Simple and Robust -- Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data -- Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees -- Inducing Polynomial Equations for Regression -- Efficient Hyperkernel Learning Using Second-Order Cone Programming -- Effective Voting of Heterogeneous Classifiers -- Convergence and Divergence in Standard and Averaging Reinforcement Learning -- Document Representation for One-Class SVM -- Naive Bayesian Classifiers for Ranking -- Conditional Independence Trees -- Exploiting Unlabeled Data in Content-Based Image Retrieval -- Population Diversity in Permutation-Based Genetic Algorithm -- Simultaneous Concept Learning of Fuzzy Rules -- Posters -- SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data -- Estimating Attributed Central Orders -- Batch Reinforcement Learning with State Importance -- Explicit Local Models: Towards "Optimal" Optimization Algorithms -- An Intelligent Model for the Signorini Contact Problem in Belt Grinding Processes -- Cluster-Grouping: From Subgroup Discovery to Clustering.
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|a Artificial intelligence.
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|a Algorithms.
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|a Mathematical logic.
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|a Database management.
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|a Artificial Intelligence.
|0 http://scigraph.springernature.com/things/product-market-codes/I21000
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|a Algorithm Analysis and Problem Complexity.
|0 http://scigraph.springernature.com/things/product-market-codes/I16021
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|a Mathematical Logic and Formal Languages.
|0 http://scigraph.springernature.com/things/product-market-codes/I16048
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|a Database Management.
|0 http://scigraph.springernature.com/things/product-market-codes/I18024
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|a Boulicaut, Jean-Francois.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Esposito, Floriana.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Giannotti, Fosca.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
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|a Pedreschi, Dino.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783662183670
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|i Printed edition:
|z 9783540231059
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|a Lecture Notes in Artificial Intelligence ;
|v 3201
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856 |
4 |
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|u https://doi.org/10.1007/b100702
|z Full Text via HEAL-Link
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912 |
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|a ZDB-2-SCS
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|a ZDB-2-LNC
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|a ZDB-2-BAE
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|a Computer Science (Springer-11645)
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