Kddi Corporation

Duc Dung Nguyen

Yasuhiro Takishima

Kazunori Matsumoto

The present invent ion provides a condensed SVM for high-speed learning using a large amount of training data. A first stage WS selector samples a plurality of training data from a training data DB, selects an optimal training vector xamong the plurality of training data, and outputs it to the WS manager. After the first stage finishes, a second stage WS selector extracts training data one by one from the training data DB and selects training data xsatisfying optimality and outputs it to the WS manager. An SVM optimizer extracts training data closest to the training data xselected by the first and second stage WS selectors from the WS being managed by the WS manager, and condenses the two first and second training data to one training data when the distance between these is smaller than a predetermined value.

Condensed SVM

An SV reduction method for multi-class SVMs is provided with which a number of SVs contained in the multi-class SVMs can be reduced without becoming trapped in a local minimum optimization solution and the reduction of the SVs can be performed at high precision and high speed. The method includes a step of selecting, from a plurality of initially present support vectors, support vector pairs z, z(i, j=1, 2, . . . , N); a step of preparing a single-variable objective function with a single global maximum and determining a maximum value k of the objective function; and a step of applying the maximum value k to the support vector pairs zand zto determine a temporary vector Ztemp[i] of small classification errors; and the support vector pairs z, zare represented by the temporary vector Ztemp[i].

Growing community of inventors

Saitama, Japan

Duc Dung Nguyen