Random Forests can Over-Exploit Classifiers in Semi-supervised Learning – We propose a novel framework for estimating adversarial examples in reinforcement learning. In particular, this framework models adversarial examples as a pairwise linear multidimensional representation of each instance, where each instance contains a given class label. Our framework uses our models to infer the model’s expected loss in some context and outputs the expected loss of the model in a nonlinear manner. We empirically analyze our framework with real-world examples and our results show that our framework is highly accurate, that we can learn an appropriate model for adversarial examples, and that our framework is very effective for classification problems with high-dimensional examples. We also verify the effectiveness of our framework in terms of the loss estimation and adversarial examples.

In this work, we investigate the possibility of a nonconvex learning method to be learned efficiently from input data. We use a nonconvex regularizer, e.g., the nonconvex logistic (NN) regularizer and a greedy minimizer, e.g., the greedy minimizer and the greedy logistic regularizer. We show that the greedy minimizer and the greedy logistic can be learned simultaneously and can learn a nonconvex regularizer to solve nonconvex optimization problems effectively. The greedy minimizer yields an efficient learning method for nonconvex learning of the kernel functions by the greedy minimizer. We also show that with respect to the optimal solution of each kernel function and the kernel, the greedy minimizer can be learned efficiently. Thus in this work, the greedy minimizer learned from input data can be used to be used as a nonconvex regularizer to learn a nonconvex kernel. We present experimental results comparing the performance of the greedy minimizer learned from a nonconvex regularizer and the greedy minimizer learned from input data.

Proximal Methods for Learning Sparse Sublinear Models with Partial Observability

Predicting the popularity of certain kinds of fruit and vegetables is NP-complete

Random Forests can Over-Exploit Classifiers in Semi-supervised Learning

Extense-aware Word Sense Disambiguation by Sparse Encoding of Word Descriptors

A Nonconvex Cost Function for Regularized Deep Belief NetworksIn this work, we investigate the possibility of a nonconvex learning method to be learned efficiently from input data. We use a nonconvex regularizer, e.g., the nonconvex logistic (NN) regularizer and a greedy minimizer, e.g., the greedy minimizer and the greedy logistic regularizer. We show that the greedy minimizer and the greedy logistic can be learned simultaneously and can learn a nonconvex regularizer to solve nonconvex optimization problems effectively. The greedy minimizer yields an efficient learning method for nonconvex learning of the kernel functions by the greedy minimizer. We also show that with respect to the optimal solution of each kernel function and the kernel, the greedy minimizer can be learned efficiently. Thus in this work, the greedy minimizer learned from input data can be used to be used as a nonconvex regularizer to learn a nonconvex kernel. We present experimental results comparing the performance of the greedy minimizer learned from a nonconvex regularizer and the greedy minimizer learned from input data.