Results 1 - 10 of 2,007

The quantum extension of the Geometric Machine Model ∗

by Renata H. S. Reiser, R. Costa
"... extends the Geometric Machine [6] Model to ob-tain a semantic modelling of quantum algorithms related to the multi-dimensional Hilbert Space [3], involving possibly infinite synchronous and non-deterministic computations based on the spatial dis-tribution of the memory states. The QGM Model provides ..."
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extends the Geometric Machine [6] Model to ob-tain a semantic modelling of quantum algorithms related to the multi-dimensional Hilbert Space [3], involving possibly infinite synchronous and non-deterministic computations based on the spatial dis-tribution of the memory states. The QGM Model

Specifying the geometric machine visual language

by Renata H. S. Reiser, Antônio C. R. Costa, Graçaliz P. Dimuro, Marcos B. Cardoso - In IEEE Symposium on Visual Languages and Formal Methods , 2003
"... This paper summarizes an experiment in the formal specification of the visual language for the Geometric Machine model [3], denoted by GMVL. The specification follows the approach proposed in the GENGED project, of the T. U. Berlin [1]. In the GMLV, supported by a visual alphabet and a visual gramma ..."
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This paper summarizes an experiment in the formal specification of the visual language for the Geometric Machine model [3], denoted by GMVL. The specification follows the approach proposed in the GENGED project, of the T. U. Berlin [1]. In the GMLV, supported by a visual alphabet and a visual

Programming in the Geometric Machine 1 Introduction.

by Renata H. S. Reiser, Antônio Carlos, R. Costa, Graçaliz P. Dimuro
"... ..."
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples

by Mikhail Belkin, Partha Niyogi, Vikas Sindhwani - JOURNAL OF MACHINE LEARNING RESEARCH , 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning al ..."
Abstract - Cited by 578 (16 self) - Add to MetaCart
algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely

A Programming Language for the Interval Geometric Machine

by Renata Hax S, Graçaliz Pereira Dimuro, Escola De Informática
"... This paper presents an interval version of the Geometric Machine Model (GMM) machine model, based on Girard’s coherence space, capable of modelling sequential, alternative, parallel (synchronous) and non-deterministic computations on a (possibly infinite) shared memory. The processes of the GMM are ..."
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This paper presents an interval version of the Geometric Machine Model (GMM) machine model, based on Girard’s coherence space, capable of modelling sequential, alternative, parallel (synchronous) and non-deterministic computations on a (possibly infinite) shared memory. The processes of the GMM

Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

by Mikhail Belkin, Partha Niyogi , 2003
"... One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing on the correspondenc ..."
Abstract - Cited by 1226 (15 self) - Add to MetaCart
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing

First steps in the construction of the Geometric Machine, em “Seleta do XXIV

by R. H. S. Reiser, A. C. R. Costa, G. P. Dimuro, Cx. P - Tendências em Matemática Aplicada e Computacional , 2002
"... Abstract. This work introduces the Geometric Machine (GM) – a computational model for the construction and representation of concurrent and non-deterministic processes, preformed in a synchronized way, with infinite memory whose positions are labelled by the points of a geometric space. The ordered ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
Abstract. This work introduces the Geometric Machine (GM) – a computational model for the construction and representation of concurrent and non-deterministic processes, preformed in a synchronized way, with infinite memory whose positions are labelled by the points of a geometric space

c ○ Uma Publicação da Sociedade Brasileira de Matemática Aplicada e Computacional. The Stochastic Geometric Machine Model 1

by R. H. S. Reiser, G. P. Dimuro, A. C. R. Costa, Escola De Informática
"... Abstract. This paper introduces the stochastic version of the Geometric Machine Model for the modelling of sequential, alternative, parallel (synchronous) and nondeterministic computations with stochastic numbers stored in a (possibly infinite) shared memory. The programming language L(D → ∞), induc ..."
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Abstract. This paper introduces the stochastic version of the Geometric Machine Model for the modelling of sequential, alternative, parallel (synchronous) and nondeterministic computations with stochastic numbers stored in a (possibly infinite) shared memory. The programming language L

Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps

by R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, F. Warner, S. Zucker - Proceedings of the National Academy of Sciences , 2005
"... of contexts of data analysis, such as spectral graph theory, manifold learning, nonlinear principal components and kernel methods. We augment these approaches by showing that the diffusion distance is a key intrinsic geometric quantity linking spectral theory of the Markov process, Laplace operators ..."
Abstract - Cited by 257 (45 self) - Add to MetaCart
of contexts of data analysis, such as spectral graph theory, manifold learning, nonlinear principal components and kernel methods. We augment these approaches by showing that the diffusion distance is a key intrinsic geometric quantity linking spectral theory of the Markov process, Laplace

Support vector machines: Training and applications

by Edgar E. Osuna, Robert Freund, Federico Girosi - A.I. MEMO 1602, MIT A. I. LAB , 1997
"... The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perc ..."
Abstract - Cited by 223 (3 self) - Add to MetaCart
of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Since Structural Risk Minimization is an inductive principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the Mean Square Error over the data set
Results 1 - 10 of 2,007