機(jī)器學(xué)習(xí)

內(nèi)容概要

The LNAI series reports state-of-the-art results in artificial intelligence re-search，development，and education，at a high level and in both printed and electronic form. Enjoying tight cooperation with the R＆D community，with numerous individuals，as well as with prestigious organizations and societies，LNAI has grown into the most comprehensive artificial intelligence research forum available.    The scope of LNAI spans the whole range of artificial intelligence and intelli-gent information processing including interdisciplinary topics in a variety ofapplication fields. The type of material published traditionally includes    proceedings（published in time for the respective conference）    post-proceedings（consisting of thoroughly revised final full papers）    research monographs（which may be based on PhD work）

書籍目錄

Contributed Papers  Convergent Gradient Ascent in General-Sum Games  Revising Engineering Models: Combining Computational Discovery  Variational Extensions to EM and Multinomial PCA  Learning and Inference for Clause Identification  An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks  Variance Optimized Bagging  How to Make AdaBoost.M1 Work for Weak Base Classifiers  Sparse Online Greedy Support Vector Regression  Pairwise Classification as an Ensemble Technique  RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood　Using Hard Classifiers to Estimate Conditional Class Probabilities  Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner  Scaling Boosting by Margin-Based Inclusion of Features and Relations  Multiclass Alternating Decision Trees　Possibilistic Induction in Decision-Tree Learning　Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains　Collaborative Learning of Term-Based Concepts for Automatic Query Expansion　Learning to Play a Highly Complex Game from Human Expert Games　Reliable Classifications with Machine Learning Matja2 Kukar and Igor Kononenko　Robustness Analyses of Instance-Based Collaborative Recommendation　iBoost: Boosting Using　an instance-Based Exponential Weighting Scheme　Towards a Simple Clustering Criterion Based　on Minimum Length Encoding　Class Probability Estimation and Cost-Sensitive Classification Decisions　On-Line Support Vector Machine Regression　Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning　A Multistrategy Approach to the Classification　of Phases in Business Cycles　A Robust Boosting Algorithm　Case Exchange Strategies in Multiagent Learning　Inductive Confidence Machines for Regression　Macro-Operators in Multirelational Learning A Search-Space Reduction Technique……Invited PapersAuthor Index

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