Titlebook: Machine Learning: ECML-95; 8th European Confere Nada Lavrac,Stefan Wrobel Conference proceedings 1995 Springer-Verlag Berlin Heidelberg 199

colony

The effect of numeric features on the scalability of inductive learning programs,amined discrete and finite feature spaces. In order to test these results, a set of experiments was carried out, involving one artificial and two real data sets. The artificial data set introduces a near-worst-case situation for the examined algorithms, while the real data sets provide an indication of their average-case behaviour.

Chameleon

Reasoning and learning in probabilistic and possibilistic networks: An overview,learning such networks from data..Whereas Bayesian networks and Markov networks are well-known for a couple of years, we also outline the perspectives of possibilistic networks as a tool for the efficient information-compressed treatment of uncertain . imprecise knowledge.

micronutrients

Pruning multivariate decision trees by hyperplane merging,ional decision trees. Nearly unexplored remains the large domain of . methods, where a new decision test (derived from previous decision tests) replaces a subtree. This paper presents an approach to multivariate-tree pruning based on merging the decision hyperplanes, and demonstrates its performance on artificial and benchmark data.

Employee

cocoon

0302-9743 e papers address all current aspects in the area of machine learning; also logic programming, planning, reasoning, and algorithmic issues are touched upon.978-3-540-59286-0978-3-540-49232-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

inferno

Conference proceedings 1995 four invited papers the volume presents revised versions of 14 long papers and 26 short papers selected from a total of 104 submissions. The papers address all current aspects in the area of machine learning; also logic programming, planning, reasoning, and algorithmic issues are touched upon.

清晰

Reasoning and learning in probabilistic and possibilistic networks: An overview,of probabilistic and possibilistic networks, respectively, and consider knowledge representation and independence as well as evidence propagation and learning such networks from data..Whereas Bayesian networks and Markov networks are well-known for a couple of years, we also outline the perspectives

AGONY

SPECT

Aromatic

Learning abstract planning cases,om given concrete cases. For this purpose, we have developed a new abstraction methodology that allows to completely . of a planning case, when the concrete and abstract languages are given by the user. Furthermore, we present a learning algorithm which is correct and complete with respect to the in