Sciweavers

72 search results - page 7 / 15
» A competitive and cooperative learning approach to robust da...
Sort
View
TNN
1998
76views more  TNN 1998»
14 years 11 months ago
Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-d
— In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical lea...
Rajeev Kumar, Peter Rockett
BMCBI
2007
133views more  BMCBI 2007»
14 years 11 months ago
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. ...
Ivan G. Costa, Roland Krause, Lennart Opitz, Alexa...
ICASSP
2011
IEEE
14 years 3 months ago
Detection of anomalous events from unlabeled sensor data in smart building environments
This paper presents a robust unsupervised learning approach for detection of anomalies in patterns of human behavior using multi-modal smart environment sensor data. We model the ...
Padmini Jaikumar, Aca Gacic, Burton Andrews, Micha...
KDD
2012
ACM
247views Data Mining» more  KDD 2012»
13 years 2 months ago
Integrating meta-path selection with user-guided object clustering in heterogeneous information networks
Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potent...
Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan...
NECO
1998
168views more  NECO 1998»
14 years 11 months ago
Constructive Incremental Learning from Only Local Information
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, t...
Stefan Schaal, Christopher G. Atkeson