A New Approximate Maximal Margin Classification Algorithm Export

@article{:2001ml, abstract = {A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called ALMA\_p (Approximate Large Margin algorithm w.r.t. norm p), takes O( (p-1) / (?2 ?2 ) ) corrections to separate the data with p-norm margin larger than (1-?)?, where g is the (normalized) p-norm margin of the data. ALMA\_p avoids quadratic (or higher-order) programming methods. It is very easy to implement and is as fast as on-line algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both real-world and artificial datasets. We compared ALMA\_2 (i.e., ALMA\_p with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by ALMA\_2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, ALMA\_2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, ALMA\_p with p > 2 largely outperforms Perceptron-like algorithms, such as ALMA\_2.}, annote = {ALMA}, author = {Gentile, Claudio}, citeulike-article-id = {733028}, citeulike-linkout-0 = {http://jmlr.csail.mit.edu/papers/v2/}, journal = {Journal of Machine Learning Research}, keywords = {antiphish, bibtex-import, recommendedbyth}, local-url = {file://localhost/Users/weimer/Documents/Papers/Files/gentile01a.pdf}, month = {December}, posted-at = {2006-07-03 16:05:46}, priority = {2}, title = {A New Approximate Maximal Margin Classification Algorithm}, url = {http://jmlr.csail.mit.edu/papers/v2/}, volume = {2}, year = {2001} }

TY - JOUR ID - :2001ml L3 - citeulike-article-id:733028 N2 - A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called ALMA_p (Approximate Large Margin algorithm w.r.t. norm p), takes O( (p-1) / (?2 ?2 ) ) corrections to separate the data with p-norm margin larger than (1-?)?, where g is the (normalized) p-norm margin of the data. ALMA_p avoids quadratic (or higher-order) programming methods. It is very easy to implement and is as fast as on-line algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both real-world and artificial datasets. We compared ALMA_2 (i.e., ALMA_p with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by ALMA_2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, ALMA_2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, ALMA_p with p > 2 largely outperforms Perceptron-like algorithms, such as ALMA_2. JF - Journal of Machine Learning Research TI - A New Approximate Maximal Margin Classification Algorithm VL - 2 KW - antiphish KW - bibtex-import KW - recommendedbyth AU - Gentile, Claudio PY - 2001/12// UR - http://jmlr.csail.mit.edu/papers/v2/ ER -