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A MultiTask Learning for Classification with
A Unified Continuous Optimization Framework
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accuracy AdaBoost analysis applied approach approximation asymptotic attributes Bayesian binary boosting bound classifier clause clustering collective inference computational conditional conditional random fields consider constraints convergence convex corresponding cost cross-validation data sets decision trees defined denote error estimate evaluation example feature selection Figure Gaussian Gibbs sampling GiniSVM given graph IEEE inductive logic programming input International Conference iterations Journal of Machine kernel labels learner learning algorithm Lemma linear logistic regression loss function LSID3 Machine Learning Machine Learning Research margin Markov matrix methods minimax minimization naive Bayes Neural Networks node nonlinear obtained optimal output overfitting pairs paper parameters perceptron performance points prediction prior probabilistic problem Proceedings Proof pyramid match random rank regularization relational representation RKHS sample Section solution space statistical subset support vector machines task Theorem training data training set update values variables variance weight wvRN