Re: [ABE-L]: Seminario/Unicamp

[Ronaldo Dias <dias@ime.unicamp.br>]

>     Prezados(as):
> 
>     Nesta sexta-feira 19/09/2003 as 11hs, teremos a presenca do Prof.
> von Zuben que apresentara o seminario:
> 
> Titulo: Machine Learning in practice: Support Vector Machines and
> Ensembles
> 
> Resumo:
> Support vector machines (SVMs) comprehend a relatively new type of
> supervised learning algorithm, originally introduced by Vapnik and
> successively extended by a number of researchers. Their remarkably
> robust performance with respect to sparse and noisy data is making them
> the system of choice when one has to deal with nontrivial
> classification, regression, and even forecasting problems. SVMs tackle
> these problems by (non)linearly mapping input data into high-dimensional
> 
> feature spaces, wherein a linear decision surface is designed. SVMs
> comprise an approximate implementation of the structural risk
> minimization, which asserts that the generalization error is delimited
> by the sum of the training error and a parcel that depends on the
> Vapnik-Chervonenkis (VC) classifier dimension. By minimizing this sum,
> high generalization performance may be obtained. Another important
> feature of the support vector learning approach is that the underlying
> optimization problems are inherently convex and have no local minima,
> which comes as the result of applying Mercer's conditions on the
> characterization of kernels. Furthermore, the number of free parameters
> in an SVM does not explicitly depend upon the input dimensionality of
> the problem at hand, something very interesting when untangling complex
> input data relationships.
> By other means, ensembles involve the generation, selection, and
> linear/nonlinear combination of a set of individual tools designed to
> simultaneously cope with the same task. This is typically done through
> the variation of some configuration parameters and/or employment of
> different training procedures. Such ensembles, a.k.a. committees, should
> 
> properly integrate the knowledge embedded in the component tools
> (devised to provide redundancy), and have frequently produced more
> accurate and robust models.
> Some proposals to synthesize extended SVM models will be described,
> and the performance when dealing with prediction, regression and
> classification problems will be presented and discussed.
> 
> 
> 
> 
> 
> --
> 
>  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> | Ronaldo Dias                                    |
> | Universidade Estadual de Campinas, UNICAMP      |
> | Departamento de Estatistica, IMECC              |
> | email address: dias@ime.unicamp.br              |
> | URL: http://www.ime.unicamp.br/~dias            |
>   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> 
> 
>