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» Relevant subtask learning by constrained mixture models
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ICML
2010
IEEE
15 years 22 days ago
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...
Gregory Druck, Andrew McCallum
SIGIR
2004
ACM
15 years 5 months ago
A two-stage mixture model for pseudo feedback
Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve th...
Tao Tao, ChengXiang Zhai
93
Voted
JCP
2007
100views more  JCP 2007»
14 years 11 months ago
Extraction of Unique Independent Components for Nonlinear Mixture of Sources
—In this paper, a neural network solution to extract independent components from nonlinearly mixed signals is proposed. Firstly, a structurally constrained mixing model is introd...
Pei Gao, Li Chin Khor, Wai Lok Woo, Satnam Singh D...
ICIP
2007
IEEE
16 years 1 months ago
Color Image Superresolution Based on a Stochastic Combinational Classification-Regression Algorithm
Abstract - The proposed algorithm in this work provides superresolution for color images by using a learning based technique that utilizes both generative and discriminant approach...
Karl S. Ni, Truong Q. Nguyen
151
Voted
PAMI
2012
13 years 2 months ago
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
João Carreira, Cristian Sminchisescu