Titlebook: Artificial Neural Networks in Pattern Recognition; 6th IAPR TC 3 Intern Neamat Gayar,Friedhelm Schwenker,Cheng Suen Conference proceedings

JOG

https://doi.org/10.1007/978-3-319-11656-3classification; feature selection; information extraction; kernel methods; learning algorithms; machine l

錫箔紙

考博

定點(diǎn)

https://doi.org/10.1007/978-3-030-00051-6In this paper we investigate reinforcement learning approaches for the popular computer game .. User-defined reward functions have been applied to .(0) learning based on .-greedy strategies in the standard Tetris scenario. The numerical experiments show that reinforcement learning can significantly outperform agents utilizing fixed policies.

MAPLE

CHOIR

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162685.jpg

晚來(lái)的提名

Artificial Neural Networks in Pattern Recognition978-3-319-11656-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

Aboveboard

Large Margin Distribution Learninghe . is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches. The recent theoretical results disclosed that the . rather than a single margin is really crucial for the generalization performa

羞辱

確定無(wú)疑

Unsupervised Active Learning of CRF Model for Cross-Lingual Named Entity Recognitionformation extraction systems. Active learning has been proven to be effective in reducing manual annotation efforts for supervised learning tasks where a human judge is asked to annotate the most informative examples with respect to a given model. However, in most cases reliable human judges are not