RIDELLA , Sandro

The K-WinnerMachine (KWM) model combines unsupervised with supervised training paradigms, and builds up a family of nested classifiers that differ in their expected generalization performances. A KWM allows members of the classifier family to reject a test pattern, and predicting the rejection rate is a crucial issue to the ultimate method...

While addressing Vector Quantization (VQ) as a general paradigm for data representation, the paper adopts the K-winner Machine model as a case study, which provides a reference for analyzing both theoretical and implementation aspects. The design of vector quantizers often requires that the (often overlooked) dichotomy between 'analogue'...

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In this paper we bound the risk of the Gibbs and Bayes classifiers (GC and BC), when the prior is defined in terms of the data generating distribution, and the posterior is defined in terms of the observed one, as proposed by Catoni (2007). We deal with this problem from two different perspectives. From one side we briefly review and further...

In this paper we further develop the idea that the PAC-Bayes prior can be defined based on the data-generating distribution. In particular, following Catoni [1], we refine some recent generalisation bounds on the risk of the Gibbs Classifier, when the prior is defined in terms of the data generating distribution, and the posterior is defined in...

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Based on Fricke's work, a more general expression for a suspension of non-interacting ellipsoidal bodies is obtained. Practical expressions for the shape factors in the case of spheroidal bodies are given.

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In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implementation on a field programmable gate array (FPGA). We analyze briefly the quantization effects on the performance of the SVM in classification problems to show its robustness, in the feedforward phase, respect to fixed-point math...

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In this paper we present a new method for solving multiclass problems with a Support Vector Machine. Our method compares favorably with other proposals, appeared so far in the literature, both in terms of computational needs for the feedforward phase and of classification accuracy. The main result, however, is the mapping of the multiclass...

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The problem of how to effectively implement k-fold cross-validation for support vector machines is considered. Indeed, despite the fact that this selection criterion is widely used due to its reasonable requirements in terms of computational resources and its good ability in identifying a well performing model, it is not clear how one should...

The well-known bounds on the generalizationability of learning machines, based on the Vapnik–Chernovenkis (VC) dimension,are very loose when applied to Support Vector Machines (SVMs).In this work we evaluate the validity of the assumption that these bounds are,nevertheless, good indicators of the generalization ability of SVMs.We show that this...

In this paper we review some basic concepts of the theory of Support Vedor Machines and derive some result of pradical interest on their generalization ability. We compare the effectiveness and efficiency in solving some well-known pattern recognition problems through the use of different kernel fundions.

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