Výsledky vyhledávání - "Dario Sterpi"

Feed-Forward Support Vector Machine Without Multipliers

Autor: S. Pischiutta, Dario Sterpi, Sandro Ridella, Davide Anguita

Publikováno v: IEEE Transactions on Neural Networks. 17:1328-1331

In this letter, we propose a coordinate rotation digital computer (CORDIC)-like algorithm for computing the feed-forward phase of a support vector machine (SVM) in fixed-point arithmetic, using only shift and add operations and avoiding resource-cons

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::133cb1643217f5c53596fb970fecc8be
https://doi.org/10.1109/tnn.2006.877537

Zobrazit plný text záznamu

Testing the Augmented Binary Multiclass SVM on Microarray Data

Autor: Davide Anguita, Sandro Ridella, Dario Sterpi

Publikováno v: IJCNN

In this paper we test a new multicategory SVM method, called augmented binary (AB), on microarray gene expression data. The AB SVM is one of the methods generating a multicategory classifier in one step, without dividing the multiclass problem into b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f716e8b8d62ea95189f3d06959ea165a
https://doi.org/10.1109/ijcnn.2006.1716351

Zobrazit plný text záznamu

Nature Inspiration for Support Vector Machines

Autor: Dario Sterpi, Davide Anguita

Publikováno v: Scopus-Elsevier
Lecture Notes in Computer Science ISBN: 9783540465379
KES (2)

We propose in this paper a new kernel, suited for Support Vector Machines learning, which is inspired from the biological world. The kernel is based on Gabor filters that are a good model for the response of the cells in the primary visual cortex and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48cede8f5b7a6d1126b0e452aa7eedb0
http://hdl.handle.net/11567/536345

Zobrazit plný text záznamu

A New Method for Multiclass Support Vector Machines

Autor: Dario Sterpi, Sandro Ridella, Davide Anguita

Publikováno v: Scopus-Elsevier

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84d63ad61593bcbf81d01ba3dce38ec3
http://hdl.handle.net/11567/315669

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání