Titlebook: Algorithmic Learning Theory; 24th International C Sanjay Jain,Rémi Munos,Thomas Zeugmann Conference proceedings 2013 Springer-Verlag Berlin

僵局

書目名稱Algorithmic Learning Theory影響因子(影響力)




書目名稱Algorithmic Learning Theory影響因子(影響力)學(xué)科排名




書目名稱Algorithmic Learning Theory網(wǎng)絡(luò)公開度




書目名稱Algorithmic Learning Theory網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Algorithmic Learning Theory被引頻次




書目名稱Algorithmic Learning Theory被引頻次學(xué)科排名




書目名稱Algorithmic Learning Theory年度引用




書目名稱Algorithmic Learning Theory年度引用學(xué)科排名




書目名稱Algorithmic Learning Theory讀者反饋




書目名稱Algorithmic Learning Theory讀者反饋學(xué)科排名

使熄滅

Conference proceedings 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: on

講個故事逗他

foodstuff

CHAFE

Learning and Optimizing with Preferencesat are the advantages of preferences compared to other forms of information, and what combinatorial and learning theoretical challenges do they give rise to? I will present important problems and survey results.

GLOOM

Exact Learning from Membership Queries: Some Techniques, Results and New Directionsdentifying, Active Learning, Guessing Game, Testing, Functional Verification, Hitting Set and Black Box PIT from Substitution or Membership Queries..In this survey we give some of the results known from the literature, different techniques used mainly for the problem of exact learning and new directions that we think are worth investigating.

向下五度才偏

Conference proceedings 2013line learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory, Bayesian/stochastic learning, and unsupervised/semi-supervised learning.

觀點(diǎn)

勾引

,Editors’ Introduction,ime several models of learning have been developed which study different aspects of learning. In the following we describe in brief the invited talks and the contributed papers for ALT 2013 held in Singapore.

符合你規(guī)定

Efficient Algorithms for Combinatorial Online Predictionin finite but exponentially many concepts and hence the complexity issue arises. In this paper, we survey some recent results on universal and efficient implementations of low-regret algorithmic frameworks such as Follow the Regularized Leader (FTRL) and Follow the Perturbed Leader (FPL).