colony
发表于 2025-3-23 16:36:03
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
发表于 2025-3-23 20:22:56
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
发表于 2025-3-24 00:21:03
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
发表于 2025-3-24 05:32:16
http://reply.papertrans.cn/63/6208/620758/620758_15.png
cocoon
发表于 2025-3-24 09:51:57
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
发表于 2025-3-24 14:08:35
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
发表于 2025-3-24 17:40:09
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
发表于 2025-3-24 20:28:06
http://reply.papertrans.cn/63/6208/620758/620758_19.png
SPECT
发表于 2025-3-25 02:20:50
http://reply.papertrans.cn/63/6208/620758/620758_20.png
Aromatic
发表于 2025-3-25 04:00:32
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