TNN
2011
12 years 11 months ago
2011
—In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from ...
ICDM
2010
IEEE
13 years 2 months ago
2010
IEEE
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
BMVC
2010
13 years 2 months ago
2010
Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they ...
NN
2008
Springer
13 years 4 months ago
2008
Springer
This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE). This approach creates neural network ensembles in an innovative ...
IJON
2002
13 years 4 months ago
2002
We consider the origin of the high-dimensional input space as a variable which can be optimized before or during neuronal learning. This set of variables acts as a translation on ...
OR
2006
Springer
13 years 4 months ago
2006
Springer
In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and...
KES
2006
Springer
13 years 4 months ago
2006
Springer
In this paper, in order to reduce the explosive increase of the search space as the input dimension grows, we present a new representation method for the structure of fuzzy rules, ...
JMLR
2008
13 years 4 months ago
2008
In this paper we are concerned with reproducing kernel Hilbert spaces HK of functions from an input space into a Hilbert space Y, an environment appropriate for multi-task learnin...
NIPS
2000
13 years 5 months ago
2000
An active set strategy is applied to the dual of a simple reformulation of the standard quadratic program of a linear support vector machine. This application generates a fast new...
AAAI
2007
13 years 6 months ago
2007
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...