Results 1  10
of
2,479
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
Abstract

Cited by 839 (85 self)
 Add to MetaCart
), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm
Kernels and Regularization on Graphs
, 2003
"... We introduce a family of kernels on graphs based on the notion of regularization operators. This generalizes in a natural way the notion of regularization and Greens functions, as commonly used for real valued functions, to graphs. It turns out that di#usion kernels can be found as a special cas ..."
Abstract

Cited by 244 (11 self)
 Add to MetaCart
We introduce a family of kernels on graphs based on the notion of regularization operators. This generalizes in a natural way the notion of regularization and Greens functions, as commonly used for real valued functions, to graphs. It turns out that di#usion kernels can be found as a special
Efficient Additive Kernels via Explicit Feature Maps
"... Maji and Berg [13] have recently introduced an explicit feature map approximating the intersection kernel. This enables efficient learning methods for linear kernels to be applied to the nonlinear intersection kernel, expanding the applicability of this model to much larger problems. In this paper ..."
Abstract

Cited by 245 (9 self)
 Add to MetaCart
we generalize this idea, and analyse a large family of additive kernels, called homogeneous, in a unified framework. The family includes the intersection, Hellinger’s, and χ2 kernels commonly employed in computer vision. Using the framework we are able to: (i) provide explicit feature maps for all
Consistency of the group lasso and multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2007
"... We consider the leastsquare regression problem with regularization by a block 1norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1norm where all spaces have dimension one, where it ..."
Abstract

Cited by 274 (33 self)
 Add to MetaCart
are replaced by functions and reproducing kernel Hilbert norms, the problem is usually referred to as multiple kernel learning and is commonly used for learning from heterogeneous data sources and for non linear variable selection. Using tools from functional analysis, and in particular covariance operators
A Kernel Method for MultiLabelled Classification
 In Advances in Neural Information Processing Systems 14
, 2001
"... This article presents a Support Vector Machine (SVM) like learning system to handle multilabel problems. Such problems are usually decomposed into many twoclass problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margi ..."
Abstract

Cited by 227 (0 self)
 Add to MetaCart
margin ranking system that shares a lot of common properties with SVMs. We tested it on a Yeast gene functional classification problem with positive results.
Efficient SVM training using lowrank kernel representations
 Journal of Machine Learning Research
, 2001
"... SVM training is a convex optimization problem which scales with the training set size rather than the feature space dimension. While this is usually considered to be a desired quality, in large scale problems it may cause training to be impractical. The common techniques to handle this difficulty ba ..."
Abstract

Cited by 240 (3 self)
 Add to MetaCart
SVM training is a convex optimization problem which scales with the training set size rather than the feature space dimension. While this is usually considered to be a desired quality, in large scale problems it may cause training to be impractical. The common techniques to handle this difficulty
Sequential Monte Carlo Samplers
, 2002
"... In this paper, we propose a general algorithm to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. A sequence of increasingly large artificial joint distributions is built; each of these distributions admits a marginal ..."
Abstract

Cited by 303 (44 self)
 Add to MetaCart
In this paper, we propose a general algorithm to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. A sequence of increasingly large artificial joint distributions is built; each of these distributions admits a marginal
The kernel trick for distances
 TR MSR 200051, Microsoft Research
, 1993
"... A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distancebased algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as normbased dista ..."
Abstract

Cited by 114 (0 self)
 Add to MetaCart
based distances in Hilbert spaces. It turns out that common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. As well as providing a useful new insight into how these algorithms work, the present work can form the basis
The Spring Kernel: A New Paradigm for RealTime Systems
 IEEE Software
, 1991
"... Next generation realtime systems will require greater flexibility and predictability than is commonly found in today's systems. These future systems include the space station, integrated vision/robotics/AI systems, collections of humans/robots coordinating to achieve common objectives (usuall ..."
Abstract

Cited by 211 (21 self)
 Add to MetaCart
Next generation realtime systems will require greater flexibility and predictability than is commonly found in today's systems. These future systems include the space station, integrated vision/robotics/AI systems, collections of humans/robots coordinating to achieve common objectives
Deformable kernels for early vision
, 1991
"... Early vision algorithms often have a first stage of linearfiltering that 'extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsel ..."
Abstract

Cited by 145 (10 self)
 Add to MetaCart
Early vision algorithms often have a first stage of linearfiltering that 'extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize
Results 1  10
of
2,479