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In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. ... First, we reduce the multiclass prediction problem to binary based on Conservative one-versusall others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm ... This paper provides a new perspective for online multiclass prediction with bandit setting model. ...doi:10.1109/icdm.2009.36 dblp:conf/icdm/ChenCZCZ09 fatcat:duiwvhsjxzanrppwbd63yfo3f4
2009 Ninth IEEE International Conference on Data Mining
SL2 Supervised Learning #2 Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Mode Guangyun Chen, Gang Chen, Jianwen Zhang, Shuo Chen, and ... Predicting Online Trusts using Trust Antecedent Framework Ee-Peng Lim, Viet-An Nguyen, Aixin Sun, Jing Jiang, and Hwee Hoon Tan Short Wednesday 4:30-6:40PM WSN Web and Social Network Efficient Award ...doi:10.1109/icdm.2009.151 fatcat:xvzjtpkkvbh25k5lmjslaf2jdi
We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of K classes and lie in the d-dimensional Euclidean space. ... Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linearly separable by a margin γ. ... Guangyun Chen, Gang Chen, Jianwen Zhang, Shuo Chen, and Changshui Zhang. Beyond banditron: A conservative and efficient reduction for online multiclass prediction with bandit setting model. ...arXiv:1902.02244v2 fatcat:tlwtx7ojs5hw5j46jm6olfehwu