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» Learning Mid-Level Features For Recognition
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CVPR
2010
IEEE
13 years 7 months ago
Modeling pixel means and covariances using factorized third-order boltzmann machines
Learning a generative model of natural images is a useful way of extracting features that capture interesting regularities. Previous work on learning such models has focused on me...
Marc Aurelio Ranzato, Geoffrey E. Hinton
ADS
2004
Springer
13 years 11 months ago
Neural Architecture for Temporal Emotion Classification
Abstract. In this pilot study, a neural architecture for temporal emotion recognition from image sequences is proposed. The investigation aims at the development of key principles ...
Roland Schweiger, Pierre Bayerl, Heiko Neumann
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 8 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
WWW
2008
ACM
14 years 8 months ago
Online learning from click data for sponsored search
Sponsored search is one of the enabling technologies for today's Web search engines. It corresponds to matching and showing ads related to the user query on the search engine...
Massimiliano Ciaramita, Vanessa Murdock, Vassilis ...
MM
2010
ACM
238views Multimedia» more  MM 2010»
13 years 7 months ago
Supervised manifold learning for image and video classification
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...
Yang Liu, Yan Liu, Keith C. C. Chan