Now showing items 1-19 of 19

    • A Connection Between GRBF and MLP 

      Maruyama, Minoru; Girosi, Federico; Poggio, Tomaso (1992-04-01)
      Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper is to show that for ...
    • Continuous Stochastic Cellular Automata that Have a Stationary Distribution and No Detailed Balance 

      Poggio, Tomaso; Girosi, Federico (1990-12-01)
      Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measures that have a non-reversible dynamic behavior. Practical applications include more ...
    • Convergence Rates of Approximation by Translates 

      Girosi, Federico; Anzellotti, Gabriele (1992-03-01)
      In this paper we consider the problem of approximating a function belonging to some funtion space Φ by a linear comination of n translates of a given function G. Ussing a lemma by Jones (1990) and Barron (1991) we show ...
    • An Equivalence Between Sparse Approximation and Support Vector Machines 

      Girosi, Federico (1997-05-01)
      In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, ...
    • Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering 

      Poggio, Tomaso; Girosi, Federico (1990-04-01)
      The theory developed in Poggio and Girosi (1989) shows the equivalence between regularization and a class of three-layer networks that we call regularization networks or Hyper Basis Functions. These networks are also ...
    • Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples 

      Girosi, Federico; Poggio, Tomaso; Caprile, Bruno (1990-07-01)
      Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization ...
    • Forecasting Global Temperature Variations by Neural Networks 

      Miyano, Takaya; Girosi, Federico (1994-08-01)
      Global temperature variations between 1861 and 1984 are forecast usingsregularization networks, multilayer perceptrons and linearsautoregression. The regularization network, optimized by stochasticsgradient descent associated ...
    • Models of Noise and Robust Estimates 

      Girosi, Federico (1991-11-01)
      Given n noisy observations g; of the same quantity f, it is common use to give an estimate of f by minimizing the function Eni=1(gi-f)2. From a statistical point of view this corresponds to computing the Maximum likelihood ...
    • Networks and the Best Approximation Property 

      Girosi, Federico; Poggio, Tomaso (1989-10-01)
      Networks can be considered as approximation schemes. Multilayer networks of the backpropagation type can approximate arbitrarily well continuous functions (Cybenko, 1989; Funahashi, 1989; Stinchcombe and White, 1989). We ...
    • A Nondeterministic Minimization Algorithm 

      Caprile, Bruno; Girosi, Federico (1990-09-01)
      The problem of minimizing a multivariate function is recurrent in many disciplines as Physics, Mathematics, Engeneering and, of course, Computer Science. In this paper we describe a simple nondeterministic algorithm ...
    • Notes on PCA, Regularization, Sparsity and Support Vector Machines 

      Poggio, Tomaso; Girosi, Federico (1998-05-01)
      We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. ...
    • On the Noise Model of Support Vector Machine Regression 

      Pontil, Massimiliano; Mukherjee, Sayan; Girosi, Federico (1998-10-01)
      Support Vector Machines Regression (SVMR) is a regression technique which has been recently introduced by V. Vapnik and his collaborators (Vapnik, 1995; Vapnik, Golowich and Smola, 1996). In SVMR the goodness of fit ...
    • On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions 

      Niyogi, Partha; Girosi, Federico (1994-02-01)
      In this paper, we bound the generalization error of a class of Radial Basis Function networks, for certain well defined function learning tasks, in terms of the number of parameters and number of examples. We show ...
    • Parallel and Deterministic Algorithms for MRFs: Surface Reconstruction and Integration 

      Geiger, Davi; Girosi, Federico (1989-05-01)
      In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive ...
    • Priors Stabilizers and Basis Functions: From Regularization to Radial, Tensor and Additive Splines 

      Girosi, Federico; Jones, Michael; Poggio, Tomaso (1993-06-01)
      We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this ...
    • Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification 

      Marroquin, Jose L.; Girosi, Federico (1993-01-01)
      In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state ...
    • Sparse Correlation Kernel Analysis and Reconstruction 

      Papgeorgiou, Constantine P.; Girosi, Federico; Poggio, Tomaso (1998-05-01)
      This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis ...
    • Support Vector Machines: Training and Applications 

      Osuna, Edgar; Freund, Robert; Girosi, Federico (1997-03-01)
      The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique ...
    • A Theory of Networks for Appxoimation and Learning 

      Poggio, Tomaso; Girosi, Federico (1989-07-01)
      Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that ...