期刊全称 | Algorithmic Learning Theory | 期刊简称 | 15th International C | 影响因子2023 | Shoham Ben-David,John Case,Akira Maruoka | 视频video | http://file.papertrans.cn/153/152986/152986.mp4 | 发行地址 | Includes supplementary material: | 学科分类 | Lecture Notes in Computer Science | 图书封面 |  | 影响因子 | Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of | Pindex | Conference proceedings 2004 |
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