Results 1 - 10 of 36,577

Learning Autonomous Navigation Abilities Using Hyper Basis Functions Networks

by M. Aste, B. Caprile , 1992
"... A system that learns how to react to visual inputs in order to accomplish simple autonomous navigation tasks is presented. The technique of Hyper Basis Functions Networks along with the use that of it can be made in problems of learning from examples is first outlined, and the various stages of the ..."
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A system that learns how to react to visual inputs in order to accomplish simple autonomous navigation tasks is presented. The technique of Hyper Basis Functions Networks along with the use that of it can be made in problems of learning from examples is first outlined, and the various stages

Reconstruction and Representation of 3D Objects with Radial Basis Functions

by J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, T. R. Evans - Computer Graphics (SIGGRAPH ’01 Conf. Proc.), pages 67–76. ACM SIGGRAPH , 2001
"... We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs al ..."
Abstract - Cited by 505 (1 self) - Add to MetaCart
We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs

Entropy-Based Algorithms For Best Basis Selection

by Ronald R. Coifman, Mladen Victor Wickerhauser - IEEE Transactions on Information Theory , 1992
"... pretations (position, frequency, and scale), and we have experimented with feature-extraction methods that use best-basis compression for front-end complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear transformat ..."
Abstract - Cited by 675 (20 self) - Add to MetaCart
the class of functionals usable by the method, speeds the best-basis search, and provides a geom

Sparse coding with an overcomplete basis set: a strategy employed by V1

by Bruno A. Olshausen, David J. Fieldt - Vision Research , 1997
"... The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and ban@ass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive f ..."
Abstract - Cited by 958 (9 self) - Add to MetaCart
The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and ban@ass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive

Monads for functional programming

by Philip Wadler , 1995
"... The use of monads to structure functional programs is described. Monads provide a convenient framework for simulating effects found in other languages, such as global state, exception handling, output, or non-determinism. Three case studies are looked at in detail: how monads ease the modification o ..."
Abstract - Cited by 1487 (43 self) - Add to MetaCart
The use of monads to structure functional programs is described. Monads provide a convenient framework for simulating effects found in other languages, such as global state, exception handling, output, or non-determinism. Three case studies are looked at in detail: how monads ease the modification

Regularization Theory and Neural Networks Architectures

by Federico Girosi, Michael Jones, Tomaso Poggio - Neural Computation , 1995
"... We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial Ba ..."
Abstract - Cited by 395 (32 self) - Add to MetaCart
to different classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends Radial Basis Functions (RBF) to Hyper Basis Functions (HBF) also leads from additive models

An integrative theory of prefrontal cortex function.

by Earl K Miller , Jonathan D Cohen - Annual Review of Neuroscience, , 2001
"... Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active ..."
Abstract - Cited by 1093 (20 self) - Add to MetaCart
Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from

Multivariate adaptive regression splines

by Jerome H. Friedman - The Annals of Statistics , 1991
"... A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automaticall ..."
Abstract - Cited by 700 (2 self) - Add to MetaCart
A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations

A training algorithm for optimal margin classifiers

by Bernhard E. Boser, et al. - PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY , 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract - Cited by 1865 (43 self) - Add to MetaCart
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters

Notions of Computation and Monads

by Eugenio Moggi , 1991
"... The i.-calculus is considered a useful mathematical tool in the study of programming languages, since programs can be identified with I-terms. However, if one goes further and uses bn-conversion to prove equivalence of programs, then a gross simplification is introduced (programs are identified with ..."
Abstract - Cited by 867 (15 self) - Add to MetaCart
with total functions from calues to values) that may jeopardise the applicability of theoretical results, In this paper we introduce calculi. based on a categorical semantics for computations, that provide a correct basis for proving equivalence of programs for a wide range of notions of computation.
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