Titlebook: Algorithmic Learning Theory; 17th International C José L. Balcázar,Philip M. Long,Frank Stephan Conference proceedings 2006 Springer-Verlag

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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




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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




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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

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苦澀

e-Science and the Semantic Web: A Symbiotic Relationshipfrastructure that enables this [4]. Scientific progress increasingly depends on pooling know-how and results; making connections between ideas, people, and data; and finding and reusing knowledge and resources generated by others in perhaps unintended ways. It is about harvesting and harnessing the

Definitive

floaters

Data-Driven Discovery Using Probabilistic Hidden Variable Modelsative approach include (a) representing complex stochastic phenomena using the structured language of graphical models, (b) using latent (hidden) variables to make inferences about unobserved phenomena, and (c) leveraging Bayesian ideas for learning and prediction. This talk will begin with a brief

Hla461

Reinforcement Learning and Apprenticeship Learning for Robotic Controlrn reinforcement learning algorithms. Some of the reasons for these problems being challenging are (i) It can be hard to write down, in closed form, a formal specification of the control task (for example, what is the cost function for “driving well”?), (ii) It is often difficult to learn a good mod

MAG

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On Exact Learning Halfspaces with Random Consistent Hypothesis Oracleunterexamples received from the equivalence query oracle. We use the RCH oracle to give a new polynomial time algorithm for exact learning halfspaces from majority of halfspaces and show that its query complexity is less (by some constant factor) than the best known algorithm that learns halfspaces

maudtin

Active Learning in the Non-realizable Caseet function that perfectly classifies all training and test examples. This assumption can hardly ever be justified in practice. In this paper, we study how relaxing the realizability assumption affects the sample complexity of active learning. First, we extend existing results on query learning to s

Factorable