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Browsing CBCL Memos (1993 - 2004) by Title

Research and Teaching Output of the MIT Community

Browsing CBCL Memos (1993 - 2004) by Title

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  • Jones, Michael J.; Poggio, Tomaso (1996-12-01)
    We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call ...
  • Jones, Michael J.; Poggio, Tomaso (1996-01-18)
    We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called ...
  • Riesenhuber, Maximilian; Poggio, Tomaso (1998-03-01)
    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 ...
  • Kim, Adlar J.; Shelton, Christian R. (2002-06-01)
    Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling ...
  • Perez-Breva, Luis; Yoshimi, Osamu (2002-12-01)
    A difficulty in the design of automated text summarization algorithms is in the objective evaluation. Viewing summarization as a tradeoff between length and information content, we introduce a technique based on ...
  • Yeo, Gene; Poggio, Tomaso (2001-08-25)
    A novel approach to multiclass tumor classification using Artificial Neural Networks (ANNs) was introduced in a recent paper cite{Khan2001}. The method successfully classified and diagnosed small, round blue cell tumors ...
  • Mukherjee, Sayan; Vapnik, Vladimir (1999-04-01)
    We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. ...
  • Jordan, Michael I.; Bishop, Christopher M. (1996-03-13)
    We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view ...
  • Riesenhuber, Maximilian; Poggio, Tomaso (1999-12-17)
    We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman. The power and flexibility ...
  • Rifkin, Ryan; Pontil, Massimiliano; Verri, Alessandro (1999-08-11)
    When training Support Vector Machines (SVMs) over non-separable data sets, one sets the threshold $b$ using any dual cost coefficient that is strictly between the bounds of $0$ and $C$. We show that there exist SVM ...
  • Evgeniou, Theodoros; Pontil, Massimiliano (2000-05-01)
    We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The ...
  • 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. ...
  • Mohan, Anuj (1999-08-11)
    In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is ...
  • Poggio, Tomaso; Hurlbert, Anya (1993-12-01)
    This paper sketches a hypothetical cortical architecture for visual 3D object recognition based on a recent computational model. The view-centered scheme relies on modules for learning from examples, such as Hyperbf-like ...
  • Jordan, Michael; Xu, Lei (1995-04-21)
    "Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a ...
  • Stein, Gideon P.; Shashua, Amnon (1997-12-01)
    This paper investigates the linear degeneracies of projective structure estimation from point and line features across three views. We show that the rank of the linear system of equations for recovering the trilinear tensor ...
  • Jaakkola, Tommi; Jordan, Michael I.; Singh, Satinder P. (1993-08-01)
    Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton ...
  • Schneider, Robert; Riesenhuber, Maximilian (2004-01-14)
    Numerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural ...
  • 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 ...
  • Evgeniou, Theodoros; Pontil, Massimiliano (1999-05-01)
    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, ...
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