Learning kernel logistic regression in the presence of class label noise
We propose an algorithm to learn the new robust multiple kernel logistic regression. The algorithm bypasses cross validation which is sub-optimal in noisy label settings. The algorithm is significantly faster than traditional cross validation approach. We empirically show that symmetric label noise can be as harmful as asymmetric noise.
lr logistic regression rlr robust logistic regression klr kernel logistic regression rklr robust kernel logistic regression rmklr robust multiple kernel logistic regression