@Article{franc-prj03, IS = { zkontrolovano 07 Dec 2003 }, UPDATE = { 2003-08-22 }, author = {Franc, Vojt{\v e}ch and Hlav{\'a}{\v c}, V{\' a}clav}, title = {An iterative algorithm learning the maximal margin classifier}, journal = {Pattern recognition}, year = {2003}, volume = {36}, number = {9}, pages = {1985--1996}, month = {September}, project = {CTU 0208313, GACR 102/00/1679, IST-2001-32184 ActIPret, IST-2001-33266 ISAAC, MSM 212300013}, publisher = {Elsevier Science}, issn = {0031-3203}, address = {Amsterdam, The Netherlands}, keywords = {Pattern recognition, Linear classifier, Suppervised learning, Support Vector Machines, Kernel functions}, annote = { A simple learning algorithm for maximal margin classifiers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger--Kozinec algorithm (S-K-algorithm) from 1981 which finds a maximal margin hyperplane with a given precision for separable data. We suggest a generalization of the S-K-algorithm (i) to the non-linear case using kernel functions and (ii) for non-separable data. The requirement in memory storage is linear to the data. This property allows the proposed algorithm to be used for large training problems. The resulting algorithm is simple to implement and as the experiments showed competitive to the state-of-the-art algorithms. The implementation of the algorithm in Matlab is available. We tested the algorithm on the problem aiming at recognition poor quality numerals. }, psurl = { [Franc-PR2003.pdf] }, }