Titlebook: Machine Learning: ECML‘97; 9th European Confere Maarten Someren,Gerhard Widmer Conference proceedings 1997 Springer-Verlag Berlin Heidelber

Munificent

Integrated learning and planning based on truncating temporal differences,time steps may be required before this trial- and-error process converges to a satisfactory policy. It is highly desirable that the number of experiences needed by the system to learn to perform its task be minimized, particularly if making errors costs much. One approach to achieve this goal is to

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Classification by Voting Feature Intervals,e participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the ., which also considers each feature separately. Experiments on real-world datasets show that VFI achieves compar

outrage

Constructing intermediate concepts by decomposition of real functions,uring of the learning problem is that it makes the learning easier and improves the comprehensibility of induced descriptions. In this paper, we develop a technique for discovering useful intermediate concepts when both the class and the attributes are real-valued. The technique is based on a decomp

instill

,Conditions for Occam’s razor applicability and noise elimination,e problem domain and has the highest prediction accuracy when classifying new instances. This principle is implicitly used also for dealing with noise, in order to avoid overfitting a noisy training set by rule truncation or by pruning of decision trees. This work gives a theoretical framework for t

結(jié)束

Learning different types of new attributes by combining the neural network and iterative attribute mbinations. Most are only capable of constructing relatively smaller new attributes. Though it is impossible to build a learner to learn any arbitrarily large and complex concept, there are some large and complex concepts that could be represented in a simple relation such as prototypical concepts,

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Learning Linear Constraints in Inductive Logic Programming, to consider, as in the field of Constraint Logic Programming, a specific computation domain and to handle terms by taking into account their values in this domain. Nevertheless, an earlier version of our system was only able to learn constraints ..=t, where .. is a variable and . is a term. We prop