T-logistic Regression
نویسندگان
چکیده
We extend logistic regression by using t-exponential families which were introduced recently in statistical physics. We examine our algorithm for both binary classfication and multiclass classfication with both L1 and L2 regularizer. The objective function of our algorithm is non-convex, an efficient block coordinate descent optimization scheme is derived for estimating the parameters. Because of the nature of the loss function, our algorithm is tolerant to label noise. We examine our algorithm in a bunch of synthetic as well as real datasets.
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