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A Formulation for Active Learning with Applications to Object Detection
(1996-06-06)
We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to ...
Measure Fields for Function Approximation
(1993-06-01)
The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation ...
3D Object Recognition: Symmetry and Virtual Views
(1992-12-01)
Many 3D objects in the world around us are strongly constrained. For instance, not only cultural artifacts but also many natural objects are bilaterally symmetric. Thoretical arguments suggest and psychophysical ...
On the Noise Model of Support Vector Machine Regression
(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 ...
Visual Speech Synthesis by Morphing Visemes
(1999-05-01)
We present MikeTalk, a text-to-audiovisual speech synthesizer which converts input text into an audiovisual speech stream. MikeTalk is built using visemes, which are a small set of images spanning a large range of mouth ...
On the V(subscript gamma) Dimension for Regression in Reproducing Kernel Hilbert Spaces
(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, ...
Multivariate Density Estimation: An SVM Approach
(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. ...
A Unified Framework for Regularization Networks and Support Vector Machines
(1999-03-01)
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 and Model-Driven Selection Using Parallel-Line Groups
(1993-05-01)
A key problem in model-based object recognition is selection, namely, the problem of isolating regions in an image that are likely to come from a single object. This isolation can be either based solely on image data ...
Limitations of Geometric Hashing in the Presence of Gaussian Noise
(1992-10-01)
This paper presents a detailed error analysis of geometric hashing for 2D object recogition. We analytically derive the probability of false positives and negatives as a function of the number of model and image, ...