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» The Inefficiency of Batch Training for Large Training Sets
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INTERSPEECH
2010
13 years 3 months ago
Semi-supervised extractive speech summarization via co-training algorithm
Supervised methods for extractive speech summarization require a large training set. Summary annotation is often expensive and time consuming. In this paper, we exploit semisuperv...
Shasha Xie, Hui Lin, Yang Liu
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 11 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
SIGIR
2012
ACM
11 years 11 months ago
Language intent models for inferring user browsing behavior
Modeling user browsing behavior is an active research area with tangible real-world applications, e.g., organizations can adapt their online presence to their visitors browsing be...
Manos Tsagkias, Roi Blanco
ECCV
2002
Springer
14 years 10 months ago
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
Hedvig Sidenbladh, Michael J. Black, Leonid Sigal
BMCBI
2010
102views more  BMCBI 2010»
13 years 9 months ago
Peptide binding predictions for HLA DR, DP and DQ molecules
Background: MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of predicti...
Peng Wang, John Sidney, Yohan Kim, Alessandro Sett...