EUROCRYPT
2010
Springer
13 years 7 months ago
2010
Springer
We present a family of verifiable random functions which are provably secure for exponentially-large input spaces under a non-interactive complexity assumption. Prior construction...
ICPR
2010
IEEE
13 years 7 months ago
2010
IEEE
Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kerne...
ICANN
1997
Springer
13 years 8 months ago
1997
Springer
Abstract. In this paper we compare two methods for intrinsic dimensionality (ID) estimation based on optimally topology preserving maps (OTPMs). The rst one is a direct approach, w...
IJCNN
2000
IEEE
13 years 8 months ago
2000
IEEE
We introduce an algorithm for learning a local metric to a continuous input space that measures distances in terms of relevance to the processing task. The relevance is defined a...
MCS
2001
Springer
13 years 9 months ago
2001
Springer
We provide several enhancements to our previously introduced algorithm for a sequential construction of a hybrid network of radial and perceptron hidden units [6]. At each stage, ...
ECAI
2004
Springer
13 years 10 months ago
2004
Springer
In this paper we propose a novel classification algorithm that fits models of different complexity on separate regions of the input space. The goal is to achieve a balance betwee...
COMPSAC
2009
IEEE
13 years 11 months ago
2009
IEEE
—This paper considers the task of software test case generation from a large space of values of input parameters. The purpose of the paper is to create a model of software input ...
VLSID
2002
IEEE
14 years 4 months ago
2002
IEEE
This paper presents a novel technique, called input space adaptive software synthesis, for the energy and performance optimization of embedded software. The proposed technique is ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of the input space. Recently there has been a growing interest in algorithms that ...
CVPR
2006
IEEE
14 years 6 months ago
2006
IEEE
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...