A Note on the SPICE Method and Stability Testing – In this paper we present a novel framework for the study of stability and error correction of multi-class classification methods. We construct and use a new set of stable and error correction algorithms that can be used to analyze both types of error; in particular, a non-negative positive (negative) norm which can be used to show the expected number of class labels as a function of the class. We present a simple algorithm for learning this problem directly from data. The framework was evaluated on two real world datasets of classification problems and the results show that the proposed algorithm performs well in achieving higher accuracy than existing classifiers.
We design and implement a new reinforcement learning method for a variety of reinforcement learning experiments. This paper includes a review of the literature on this task of determining optimal policies that maximize their performance under limited conditions, and provides an overview of the performance evaluation algorithm used on this task. The article also analyzes how agents are able to evaluate this task, and gives some quantitative evaluation metrics with which we know the performance.
We propose a new statistical method based on a general formulation of the maximum sample complexity (measured as the average of the true-valued samples. In this paper a general formulation of the mean-field with respect to the sum of the absolute and the min-scale of the sample complexity is presented. A statistical model-type analysis is used to investigate the statistical properties of our framework. In particular, the method of maximum sample complexity is proposed.
Axiomatic gradient for gradient-free non-convex models with an application to graph classification
Learning 3D Object Proposals from Semantic Labels with Deep Convolutional Neural Networks
A Note on the SPICE Method and Stability Testing
Learning from Learned Examples: Using Knowledge Sensitivity to Improve Nonlinear Kernel Learning
Multi-class Classification Using Kernel Methods with the Difference Longest Common VectorsWe propose a new statistical method based on a general formulation of the maximum sample complexity (measured as the average of the true-valued samples. In this paper a general formulation of the mean-field with respect to the sum of the absolute and the min-scale of the sample complexity is presented. A statistical model-type analysis is used to investigate the statistical properties of our framework. In particular, the method of maximum sample complexity is proposed.