Learning with Augmented Class by Exploiting Unlabeled Data

First Page		Document Content
Date: 2016-01-04 03:15:06 Machine learning Learning Artificial intelligence Support vector machine Kernel method Semi-supervised learning Sequential minimal optimization Anomaly detection Transduction Outlier Supervised learning Binary classification		Learning with Augmented Class by Exploiting Unlabeled Data Add to Reading List Source URL: cs.nju.edu.cn Download Document from Source Website File Size: 452,70 KB Share Document on Facebook

	A framework for 3D object recognition using the kernel constrained mutual subspace method Kazuhiro Fukui1 , Bj¨orn Stenger2 , and Osamu Yamaguchi2 1 Graduate School of Systems and Information Engineering, University of DocID: 1vrKr - View Document
	A Framework for 3D Object Recognition using the Kernel Constrained Mutual Subspace Method Kazuhiro Fukui1, Bjorn Stenger2 and Osamu Yamaguchi*2 University of Tsukuba Corporate Research & Development Center DocID: 1uWXT - View Document
	3-24 MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN Hand Shape Recognition based on Kernel Orthogonal Mutual Subspace Method DocID: 1uvN9 - View Document
	The Kernel Orthogonal Mutual Subspace Method and its Application to 3D Object Recognition Kazuhiro Fukui1 and Osamu Yamaguchi2 1 DocID: 1uuR3 - View Document
	NOTES ON HEAT KERNEL ASYMPTOTICS DANIEL GRIESER Abstract. These are informal notes on how one can prove the existence and asymptotics of the heat kernel on a compact Riemannian manifold with boundary. The method differs DocID: 1rZFh - View Document