Sciweavers

IDA
2002
Springer
13 years 3 months ago
Evolutionary model selection in unsupervised learning
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...
YongSeog Kim, W. Nick Street, Filippo Menczer
AAMAS
2002
Springer
13 years 3 months ago
Cooperative Learning Using Advice Exchange
Abstract. One of the main questions concerning learning in a Multi-Agent System's environment is: "(How) can agents benefit from mutual interaction during the learning pr...
Luís Nunes, Eugenio Oliveira
IJON
2007
73views more  IJON 2007»
13 years 3 months ago
Affordances, effectivities, and assisted imitation: Caregivers and the directing of attention
We focus on how infants’ discovery of a range of affordances and effectivities contributes to participating in a new activity. We emphasize how caregivers bracket ongoing action...
Patricia Zukow-Goldring, Michael A. Arbib
KBS
2006
150views more  KBS 2006»
13 years 3 months ago
Clusterer ensemble
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods ...
Zhi-Hua Zhou, Wei Tang
JMLR
2006
112views more  JMLR 2006»
13 years 3 months ago
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap betw...
Andrea Passerini, Paolo Frasconi, Luc De Raedt
JMLR
2008
107views more  JMLR 2008»
13 years 3 months ago
A Library for Locally Weighted Projection Regression
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensiona...
Stefan Klanke, Sethu Vijayakumar, Stefan Schaal
JMLR
2008
131views more  JMLR 2008»
13 years 3 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
CORR
2010
Springer
70views Education» more  CORR 2010»
13 years 3 months ago
Structured sparsity-inducing norms through submodular functions
Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial ...
Francis Bach
BMCBI
2010
133views more  BMCBI 2010»
13 years 3 months ago
Learning an enriched representation from unlabeled data for protein-protein interaction extraction
Background: Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used w...
Yanpeng Li, Xiaohua Hu, Hongfei Lin, Zhihao Yang
GECCO
2008
Springer
118views Optimization» more  GECCO 2008»
13 years 4 months ago
Unsupervised learning of echo state networks: balancing the double pole
A possible alternative to fine topology tuning for Neural Network (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely r...
Fei Jiang, Hugues Berry, Marc Schoenauer