Results 1 - 10 of 269

T.K.: Real-time articulated hand pose estimation using semi-supervised transductive regression forests

by Danhang Tang, Tsz-ho Yu, Tae-kyun Kim - In: Proc. ICCV (2013
"... This paper presents the first semi-supervised transduc-tive algorithm for real-time articulated hand pose estima-tion. Noisy data and occlusions are the major challenges of articulated hand pose estimation. In addition, the dis-crepancies among realistic and synthetic pose data under-mine the perfor ..."
Abstract - Cited by 26 (3 self) - Add to MetaCart
This paper presents the first semi-supervised transduc-tive algorithm for real-time articulated hand pose estima-tion. Noisy data and occlusions are the major challenges of articulated hand pose estimation. In addition, the dis-crepancies among realistic and synthetic pose data under

Semi-Supervised Classification by Low Density Separation

by Olivier Chapelle, Alexander Zien , 2005
"... We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing the Transd ..."
Abstract - Cited by 175 (9 self) - Add to MetaCart
We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing

Semi-supervised graph clustering: a kernel approach

by Brian Kulis, Sugato Basu, Inderjit Dhillon, Raymond Mooney , 2008
"... Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this ..."
Abstract - Cited by 94 (3 self) - Add to MetaCart
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors

Optimization Approaches to Semi-Supervised Learning

by Ayhan Demiriz, Kristin P. Bennett , 2000
"... We examine mathematical models for semi-supervised support vector machines (S VM). Given a training set of labeled data and a working set of unlabeled data, S VM constructs a support vector machine using both the training and working sets. We use S VM to solve the transductive inference problem pose ..."
Abstract - Cited by 19 (1 self) - Add to MetaCart
We examine mathematical models for semi-supervised support vector machines (S VM). Given a training set of labeled data and a working set of unlabeled data, S VM constructs a support vector machine using both the training and working sets. We use S VM to solve the transductive inference problem

Non-Negative Semi-Supervised Learning

by Changhu Wang , Shuicheng Yan , Lei Zhang , Hong-Jiang Zhang - Journal of Machine Learning Research
"... Abstract The contributions of this paper are three-fold. First, we present a general formulation for reaping the benefits from both non-negative data factorization and semi-supervised learning, and the solution naturally possesses the characteristics of sparsity, robustness to partial occlusions, a ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Abstract The contributions of this paper are three-fold. First, we present a general formulation for reaping the benefits from both non-negative data factorization and semi-supervised learning, and the solution naturally possesses the characteristics of sparsity, robustness to partial occlusions

Semi-Supervised Classification on Evolutionary Data ∗

by Yangqing Jia, Shuicheng Yan, Changshui Zhang
"... In this paper, we consider semi-supervised classification on evolutionary data, where the distribution of the data and the underlying concept that we aim to learn change over time due to shortterm noises and long-term drifting, making a single aggregated classifier inapplicable for long-term classif ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
space, and then derive the online algorithm that efficiently finds the closed-form solution to the classification functions. Experimental results on real-world evolutionary mailing list data demonstrate that our algorithm outperforms classical semi-supervised learning algorithms in both algorithmic

Some Contributions to Semi-Supervised Learning

by Pavan Kumar Mallapragada , 2010
"... Semi-supervised learning methods attempt to improve the performance of a supervised or an unsupervised learner in the presence of “side information”. This side information can be in the form of unlabeled samples in the supervised case or pair-wise constraints in the unsupervised case. Most existing ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
, and unsupervised feature selection, are extended to their semi-supervised counterparts. Our first contribution is an algorithm that utilizes unlabeled data along with the labeled data while training classifiers. Unlike previous approaches that design specialized algorithms to effectively exploit the labeled

Regularized Semi-supervised Classification on Manifold

by Lianwei Zhao , Siwei Luo , Yanchang Zhao , Zhihai Wang
"... Abstract. Semi-supervised learning gets estimated marginal distribution X P with a large number of unlabeled examples and then constrains the conditional probability ) | ( x y p with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The ..."
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. The label information graph is first defined to keep the pairwise label relationship and can be incorporated with neighborhood graph which reflects the intrinsic geometry structure of X P . Then we propose a novel regularized semi-supervised classification algorithm, in which the regularization term

Local Linear Semi-supervised Regression

by Mugizi Robert Rwebangira, John Lafferty , 2009
"... The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or In many machine learning application domains, obtaining labeled data is ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
regularization using local linear estimators. This is the first extension of local linear regression to the semi-supervised setting. We present experimental results on both synthetic and real data and show that this method tends to perform better than methods which only utilize the labeled

Nonparametric function induction in semi-supervised learning

by Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux - In Proc. Artificial Intelligence and Statistics , 2005
"... There has been an increase of interest for semi-supervised learning recently, because of the many datasets with large amounts of unlabeled examples and only a few labeled ones. This paper follows up on proposed nonparametric algorithms which provide an estimated continuous label for the given unlabe ..."
Abstract - Cited by 51 (5 self) - Add to MetaCart
There has been an increase of interest for semi-supervised learning recently, because of the many datasets with large amounts of unlabeled examples and only a few labeled ones. This paper follows up on proposed nonparametric algorithms which provide an estimated continuous label for the given
Results 1 - 10 of 269