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» Sampling Methods for Unsupervised Learning
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106
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CORR
2002
Springer
97views Education» more  CORR 2002»
15 years 2 months ago
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is...
Peter D. Turney
129
Voted
ICCV
2011
IEEE
14 years 2 months ago
Domain Adaptation for Object Recognition: An Unsupervised Approach
Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we p...
Raghuraman Gopalan, Ruonan Li, Rama Chellappa
122
Voted
ICVGIP
2004
15 years 3 months ago
Modeling Signs Using Functional Data Analysis
1 We present a functional data analysis (FDA) based method to statistically model continuous signs of the American Sign Language (ASL) for use in the recognition of signs in contin...
Sunita Nayak, Sudeep Sarkar, Kuntal Sengupta
87
Voted
AIPR
2000
IEEE
15 years 6 months ago
Gradient-Oriented Profiles for Unsupervised Boundary Classification
We present a method for unsupervised boundary classijication by producing and analyzing intensity profiles. Each profile is created by sampling an ellipsoidal neighborhood of voxe...
Robert J. Tamburo, George D. Stetten
102
Voted
ICML
2005
IEEE
16 years 3 months ago
Predicting relative performance of classifiers from samples
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
Rui Leite, Pavel Brazdil