ICCV
2011
IEEE
12 years 4 months ago
2011
IEEE
In the real visual world, the number of categories a classifier needs to discriminate is on the order of hundreds or thousands. For example, the SUN dataset [24] contains 899 sce...
ICANN
2011
Springer
12 years 8 months ago
2011
Springer
This paper is concerned with estimation of learning curves for Gaussian process regression with multidimensional numerical integration. We propose an approach where the recursion e...
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
A study of generalization error in signal detection by multiple spatially-distributed and -correlated sensors is provided when the detection rule is learned from a finite number ...
ML
2010
ACM
12 years 11 months ago
2010
ACM
We introduce a general-purpose learning machine that we call the Guaranteed Error Machine, or GEM, and two learning algorithms, a real GEM algorithm and an ideal GEM algorithm. Th...
JMLR
2010
12 years 11 months ago
2010
In this paper, we consider the asymptotic form of the generalization error for the restricted Boltzmann machine in Bayesian estimation. It has been shown that obtaining the maximu...
TCS
2011
12 years 11 months ago
2011
The PAC-learning model is distribution-independent in the sense that the learner must reach a learning goal with a limited number of labeled random examples without any prior know...
TCS
2010
13 years 2 months ago
2010
This paper concerns learning binary-valued functions defined on IR, and investigates how a particular type of ‘regularity’ of hypotheses can be used to obtain better generali...
NN
2002
Springer
2002
Springer
Optimal design of regularization term and regularization parameter by subspace information criterion
13 years 4 months ago
The problem of designing the regularization term and regularization parameter for linear regression models is discussed. Previously, we derived an approximation to the generalizat...
ML
2002
ACM
13 years 4 months ago
2002
ACM
For two-class datasets, we provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned ...
JMLR
2002
13 years 4 months ago
2002
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...