Evolving Minimax Functions via Stochastic Convergence Theory – We propose a general method for estimating the performance of a linear classifier, by using a single, weighted, random sample-based, linear ensemble estimator. Our method has the following advantages: (1) It is equivalent to a weighted Gaussian process; (2) It is robust to any non-linearity; and (3) It estimates the expected probability of learning a given class over the training set. We demonstrate this by using a variety of experiments where the expected probability of learning a given class over the training set is highly predictive, and the prediction error depends on the degree of belief of the classifier, which differs between the predictions obtained by the estimator and the estimators themselves. We illustrate several such scenarios in one graphical model.

In this paper, we propose a novel method of variational inference for Gaussian model using non-negative matrix factorization in lieu of non-Gaussian model. The method allows efficient and robust inference for non-Gaussian models. It is fully guaranteed to obtain good models that provide reliable predictions. We also show that our approach is efficient in general and achieves comparable performance to the previous work on Gaussian models using a non-Gaussian model.

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# Evolving Minimax Functions via Stochastic Convergence Theory

A Survey on Modeling Problems for Machine Learning

Toward Optimal Learning of Latent-Variable ModelsIn this paper, we propose a novel method of variational inference for Gaussian model using non-negative matrix factorization in lieu of non-Gaussian model. The method allows efficient and robust inference for non-Gaussian models. It is fully guaranteed to obtain good models that provide reliable predictions. We also show that our approach is efficient in general and achieves comparable performance to the previous work on Gaussian models using a non-Gaussian model.

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