DSpace at MIT
(28,129 recursos)
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83.
Belief Propagation and Revision in Networks with Loops - Weiss, Yair
Local belief propagation rules of the sort proposed by Pearl(1988) are guaranteed to converge to the optimal beliefs for singly connected networks.
87.
Statistical Models for Co-occurrence Data - Hofmann, Thomas; Puzicha, Jan
In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets.
88.
Modeling Invariances in Inferotemporal Cell Tuning - Riesenhuber, Maximilian; Poggio, Tomaso
In macaque inferotemporal cortex (IT), neurons have been found to respond selectively to complex shapes while showing broad tuning ("invariance") with respect to stimulus transformations such as translation and scale changes and a limited tuning to rotation in depth.
89.
Notes on PCA, Regularization, Sparsity and Support Vector Machines - Poggio, Tomaso; Girosi, Federico
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.
90.
Sparse Correlation Kernel Analysis and Reconstruction - Papgeorgiou, Constantine P.; Girosi, Federico; Poggio, Tomaso
The basis functions that we use are the correlation functions of the class of signals we are analyzing.
91.
Estimating Dependency Structure as a Hidden Variable - Meila, Marina; Jordan, Michael I.; Morris, Quaid
We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors.
92.
From Regression to Classification in Support Vector Machines - Pontil, Massimiliano; Rifkin, Ryan; Evgeniou, Theodoros
We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC).
93.
On the Noise Model of Support Vector Machine Regression - Pontil, Massimiliano; Mukherjee, Sayan; Girosi, Federico
In SVMR the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called Vapnik"s $epsilon$- insensitive loss function, which is similar to the "robust" loss functions introduced by Huber (Huber, 1981).
94.
Multivariate Density Estimation: An SVM Approach - Mukherjee, Sayan; Vapnik, Vladimir
We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation.
95.
A Unified Framework for Regularization Networks and Support Vector Machines - Evgeniou, Theodoros; Pontil, Massimiliano; Poggio, Tomaso
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data.
96.
On the V(subscript gamma) Dimension for Regression in Reproducing Kernel Hilbert Spaces - Evgeniou, Theodoros; Pontil, Massimiliano
This paper presents a computation of the $V_gamma$ dimension for regression in bounded subspaces of Reproducing Kernel Hilbert Spaces (RKHS) for the Support Vector Machine (SVM) regression $epsilon$-insensitive loss function, and general $L_p$ loss functions.
97.
Visual Speech Synthesis by Morphing Visemes - Ezzat, Tony; Poggio, Tomaso
MikeTalk is built using visemes, which are a small set of images spanning a large range of mouth shapes.
98.
Learning-Based Approach to Estimation of Morphable Model Parameters - Kumar, Vinay; Poggio, Tomaso
We describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation.
100.
Role of color in face recognition - Yip, Andrew; Sinha, Pawan
One of the key challenges in face perception lies in determining the contribution of different cues to face identification.
81.
