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» Learning to Classify Texts Using Positive and Unlabeled Data
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CVPR
2005
IEEE
14 years 9 months ago
Hybrid Models for Human Motion Recognition
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the h...
Claudio Fanti, Lihi Zelnik-Manor, Pietro Perona
KDD
2009
ACM
158views Data Mining» more  KDD 2009»
14 years 8 months ago
Feature shaping for linear SVM classifiers
: ? Feature Shaping for Linear SVM Classifiers George Forman, Martin Scholz, Shyamsundar Rajaram HP Laboratories HPL-2009-31R1 text classification machine learning, feature weighti...
George Forman, Martin Scholz, Shyamsundar Rajaram
NAACL
2007
13 years 9 months ago
Extracting Semantic Orientations of Phrases from Dictionary
We propose a method for extracting semantic orientations of phrases (pairs of an adjective and a noun): positive, negative, or neutral. Given an adjective, the semantic orientatio...
Hiroya Takamura, Takashi Inui, Manabu Okumura
ACL
2012
11 years 10 months ago
Named Entity Disambiguation in Streaming Data
The named entity disambiguation task is to resolve the many-to-many correspondence between ambiguous names and the unique realworld entity. This task can be modeled as a classifi...
Alexandre Davis, Adriano Veloso, Altigran Soares d...
ACL
2006
13 years 9 months ago
Machine Learning of Temporal Relations
This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning ...
Inderjeet Mani, Marc Verhagen, Ben Wellner, Chong ...