Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirab...

We consider the multi class classification problem under the setting where the number of labels is very large and hence it is very desirable to efficiently achieve train and test running times which are logarithmic in the label complexity. Additionally the labels are feature dependent in our setting. We propose a reduction of this problem to a set of binary regression problems organized in a tr...

forest classification on the basis of satellite images is a promising technique both for primary map production and for map updating and forest monitoring. for accurate for-est classification into three classes, using mapping by canopy cover density “high spatial resolution satellite images have to be used in order to obtain the required spatial detail” [schneider, 1999]. at the same time, the ...