Results 1 - 10 of 1,118

Sentiwordnet: A publicly available lexical resource for opinion mining

by Andrea Esuli, Fabrizio Sebastiani - In In Proceedings of the 5th Conference on Language Resources and Evaluation (LRECÕ06 , 2006
"... Opinion mining (OM) is a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. OM has a rich set of applications, ranging from tracking users’ opinions about products ..."
Abstract - Cited by 376 (5 self) - Add to MetaCart
of the resulting vectorial term representations for semi-supervised synset classification. The three scores are derived by combining the results produced by a committee of eight ternary classifiers, all characterized by similar accuracy levels but different classification behaviour. SENTIWORDNET is freely

Learning with local and global consistency.

by Dengyong Zhou , Olivier Bousquet , Thomas Navin Lal , Jason Weston , Bernhard Schölkopf - In NIPS, , 2003
"... Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intr ..."
Abstract - Cited by 673 (21 self) - Add to MetaCart
Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect

Semi-supervised support vector machines

by Kristin P. Bennett, Ayhan Demiriz - In Proc. NIPS , 1998
"... We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled data and a working set of unlabeled data, S3YM constructs a support vector machine us-ing both the training and working sets. We use S3YM to solve the transduction problem using overall risk minimiza ..."
Abstract - Cited by 223 (6 self) - Add to MetaCart
We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled data and a working set of unlabeled data, S3YM constructs a support vector machine us-ing both the training and working sets. We use S3YM to solve the transduction problem using overall risk

Imagenet: A large-scale hierarchical image database

by Jia Deng, Wei Dong, Richard Socher, Li-jia Li, Kai Li, Li Fei-fei - In CVPR , 2009
"... The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce her ..."
Abstract - Cited by 840 (28 self) - Add to MetaCart
here a new database called “ImageNet”, a largescale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions

Semi-Supervised Learning on Riemannian Manifolds

by Mikhail Belkin , Partha Niyogi , 2004
"... We consider the general problem of utilizing both labeled and unlabeled data to improve classification accuracy. Under the assumption that the data lie on a submanifold in a high dimensional space, we develop an algorithmic framework to classify a partially labeled data set in a principled manner. ..."
Abstract - Cited by 193 (7 self) - Add to MetaCart
We consider the general problem of utilizing both labeled and unlabeled data to improve classification accuracy. Under the assumption that the data lie on a submanifold in a high dimensional space, we develop an algorithmic framework to classify a partially labeled data set in a principled manner

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 Learning with Graphs

by Xiaojin Zhu - CARNEGIE MELLON UNIVERSITY , 2005
"... In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relat ..."
Abstract - Cited by 112 (0 self) - Add to MetaCart
be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy

On semi-supervised classification

by Balaji Krishnapuram, David Williams, Ya Xue, Alex Hartemink, Lawrence Carin, Mário A. T. Figueiredo - In , 2005
"... A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for train ..."
Abstract - Cited by 49 (10 self) - Add to MetaCart
A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm

Semi-Supervised Classification with Universum

by Dan Zhang, Jingdong Wang, Fei Wang, Changshui Zhang
"... The Universum data, defined as a collection of ”nonexamples” that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification pro ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
The Universum data, defined as a collection of ”nonexamples” that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification

On Discriminative Semi-Supervised Classification

by Fei Wang, Changshui Zhang - PROCEEDINGS OF THE TWENTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (2008) , 2008
"... The recent years have witnessed a surge of interests in semi-supervised learning methods. A common strategy for these algorithms is to require that the predicted data labels should be sufficiently smooth with respect to the intrinsic data manifold. In this paper, we argue that rather than penalizing ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
penalizing the label smoothness, we can directly punish the discriminality of the classification function to achieve a more powerful predictor, and we derive two specific algorithms: Semi-Supervised Discriminative Regularization (SSDR) and Semi-parametric Discriminative Semi-supervised Classification (SDSC
Results 1 - 10 of 1,118