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IJAR
2006
89views more  IJAR 2006»
15 years 6 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
PKAW
2010
15 years 4 months ago
MMG: A Learning Game Platform for Understanding and Predicting Human Recall Memory
How humans infer probable information from the limited observed data? How they are able to build on little knowledge about the context in hand? Is the human memory repeatedly const...
Umer Fareed, Byoung-Tak Zhang
ISAMI
2010
15 years 29 days ago
Ontology and SWRL-Based Learning Model for Home Automation Controlling
Abstract. In the present paper we describe IntelliDomo's learning model, an ontology-based expert system able to control a home automation system and to learn user's beha...
Pablo A. Valiente-Rocha, Adolfo Lozano Tello
ECCV
2010
Springer
15 years 8 months ago
Learning Shape Segmentation Using Constrained Spectral Clustering and Probabilistic Label Transfer
We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a...
KDD
2009
ACM
229views Data Mining» more  KDD 2009»
16 years 6 months ago
Relational learning via latent social dimensions
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, ...
Lei Tang, Huan Liu