Browsing AI Memos (1959 - 2004) by Author "Evgeniou, Theodoros"
Now showing items 1-7 of 7
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From Regression to Classification in Support Vector Machines
(1998-11-01)We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain ... -
Image Based Rendering Using Algebraic Techniques
(1996-11-01)This paper presents an image-based rendering system using algebraic relations between different views of an object. The system uses pictures of an object taken from known positions. Given three such images it can ... -
Image-Based View Synthesis
(1997-01-01)We present a new method for rendering novel images of flexible 3D objects from a small number of example images in correspondence. The strength of the method is the ability to synthesize images whose viewing position ... -
A Note on the Generalization Performance of Kernel Classifiers with Margin
(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 ... -
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, ... -
Sparse Representations of Multiple Signals
(1997-09-01)We discuss the problem of finding sparse representations of a class of signals. We formalize the problem and prove it is NP-complete both in the case of a single signal and that of multiple ones. Next we develop a simple ... -
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 ...
