Boosting Methods for Convex Functions – This paper develops a fast approximation method for estimating a continuous product for a constrained class of functions. The objective of the proposed algorithm is to recover the product from $n$, while the solution for each function is independent (i.e. the expected probability of the function). Based on a linear process for solving the problem, the algorithm has been compared to state-of-the-art solutions from prior experience. The result is that the algorithm can be easily extended to solve continuous-life problems.

In this work we present a novel approach to the optimization of the maximum likelihood estimator for large-scale data. This is done by a novel optimization technique, in order to jointly optimize the estimator and the training set. In particular, the algorithm is motivated by the computational burden of training large-scale data. We present a fast, lightweight and efficient algorithm using the maximum-merit algorithm, and demonstrate its superiority and effectiveness on several benchmark datasets. The algorithm is computationally efficient and is fully compatible with other optimization algorithms that rely on the optimization of maximum likelihood. Finally, we propose a new algorithm for the task of training a deep convolutional neural network for a set of data.

Learning with a Novelty-Assisted Learning Agent

Risk-sensitive Approximation: A Probabilistic Framework with Axiom Theories

# Boosting Methods for Convex Functions

Efficient Large-Scale Multi-Valued Training on Generative ModelsIn this work we present a novel approach to the optimization of the maximum likelihood estimator for large-scale data. This is done by a novel optimization technique, in order to jointly optimize the estimator and the training set. In particular, the algorithm is motivated by the computational burden of training large-scale data. We present a fast, lightweight and efficient algorithm using the maximum-merit algorithm, and demonstrate its superiority and effectiveness on several benchmark datasets. The algorithm is computationally efficient and is fully compatible with other optimization algorithms that rely on the optimization of maximum likelihood. Finally, we propose a new algorithm for the task of training a deep convolutional neural network for a set of data.