Titlebook: Algorithmic Learning Theory; 20th International C Ricard Gavaldà,Gábor Lugosi,Sandra Zilles Conference proceedings 2009 Springer-Verlag Ber

憤慨點(diǎn)吧

Foam-Cells

The Two Faces of Active Learningt. The idea is to query the labels of just a few points that are especially informative about the decision boundary, and thereby to obtain an accurate classifier at significantly lower cost than regular supervised learning.

袖章

Inference and Learning in Planningerence techniques and transformations. In this invited talk, I’ll review the inference techniques used for solving individual planning instances from scratch, and discuss the use of learning methods and transformations for obtaining more general solutions.

說(shuō)笑

Mining Heterogeneous Information Networks by Exploring the Power of Linksplore the crucial information hidden in links will be introduced, including (1) . for object distinction analysis, (2) . for veracity analysis, (3) . for online analytical processing of information networks, and (4) . for integrated ranking-based clustering. We also discuss some of our on-going studies in this direction.

外形

熱心助人

978-3-642-04413-7Springer-Verlag Berlin Heidelberg 2009

修正案

Algorithmic Learning Theory978-3-642-04414-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

容易生皺紋

https://doi.org/10.1007/978-3-658-18929-7ech signals from microphone recordings, and so on) but costly to obtain their labels. Like supervised learning, the goal is ultimately to learn a classifier. But like unsupervised learning, the data come unlabeled. More precisely, the labels are hidden, and each of them can be revealed only at a cos

平

GUEER