机器学习
2002-09-17
Springer
Tapio Elomaa
528
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）
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
