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

extemporaneous

Multiple-Knowledge Representations in concept learning,nown that biases used in learning algorithms directly affect their performance as well as their comprehensibility. A critical problem is that, most of the time, the most “comprehensible” representations are not the best performer in terms of classification! In this paper, we argue that concept learn

exophthalmos

The effect of numeric features on the scalability of inductive learning programs,resents the results of a theoretical and experimental investigation of the scalability of four well-known empirical concept learning programs. In particular it examines the effect of using numeric features in the training set. The theoretical part of the work involved a detailed worst-case computati

抱負(fù)

Analogical logic program synthesis from examples,mples, the task of our algorithm is to find a program which explains the examples correctly and is similar to the source program. Although we can define a notion of similarity in various ways, we consider a class of similarities from the viewpoint of how examples are explained by a program. In a wor

Bombast

A guided tour through hypothesis spaces in ILP,llions of hypotheses for even simple learning problems. Controlling hypothesis spaces by biases requires knowledge on the effects and applicability of biases in different domains. This knowledge can be gained experimentally by comparing the size of hypothesis spaces with respect to the language bias

Ankylo-

障礙物

Specialization of recursive predicates,tions of positive examples. In contrast to its predecessor SPECTRE, the new algorithm is not limited to specializing clauses defining one predicate only, but may specialize clauses defining multiple predicates. Furthermore, the positive and negative examples are no longer required to be instances of

Dysarthria

The power of decision tables,the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time.

小隔間

https://doi.org/10.1007/3-540-59286-5Case-Based Learning; Concept-Learning; Fallbasiertes Lernen; Genetische Algorithmen; Lern-Algorithmen; Ma

Override

彎彎曲曲

A distributed genetic algorithm improving the generalization behavior of neural networks,standard learning algorithms like e.g. backpropagation. In this paper a distributed genetic algorithm is designed and used to improve the network‘s generalization capabilities by reducing the number of different weights in the neural network.