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» On Graphical Modeling of Preference and Importance
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PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
13 years 6 months ago
Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Zhao Xu, Kristian Kersting, Thorsten Joachims
DAGM
2010
Springer
13 years 6 months ago
An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM
Abstract. Graphical models with higher order factors are an important tool for pattern recognition that has recently attracted considerable attention. Inference based on such model...
Björn Andres, Jörg H. Kappes, Ullrich K&...
ICASSP
2009
IEEE
14 years 8 days ago
Graphical Models: Statistical inference vs. determination
Using discrete Hidden-Markov-Models (HMMs) for recognition requires the quantization of the continuous feature vectors. In handwritten whiteboard note recognition it turns out tha...
Joachim Schenk, Benedikt Hörnler, Artur Braun...
TIT
2008
118views more  TIT 2008»
13 years 6 months ago
Discrete-Input Two-Dimensional Gaussian Channels With Memory: Estimation and Information Rates Via Graphical Models and Statisti
Abstract--Discrete-input two-dimensional (2-D) Gaussian channels with memory represent an important class of systems, which appears extensively in communications and storage. In sp...
Ori Shental, Noam Shental, Shlomo Shamai, Ido Kant...
ICCV
2011
IEEE
12 years 8 months ago
Learning to Cluster Using High Order Graphical Models with Latent Variables
This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
Nikos Komodakis