Neural Fisher Discriminant Analysis – Neural network models contain two main components, classification and segmentation, which are very similar but which are not easily distinguishable. Classifying the network structure can be tedious and time consuming, especially for large networks. This work tackles the task of classifying a large set of MNIST digits using neural networks (NN). We first propose a neural network model of MNIST digits which has a multi-layer perceptron for classification. Then we apply a neural network to classify MNIST digits using a multi-task learning algorithm. Experimental results demonstrate that the proposed model outperforms the state-of-the-art MNIST digits classification method.

Game mechanics and game theory, particularly those related to the game of chess, are often associated in non-linear causal structures and theories. In this paper, we present a probabilistic model for probabilistic causal structure representations of games, where games are simulated. We demonstrate that for some games, the model may be able to infer causal structures from random state values with an accuracy of near-optimal, considering that the causal structure is often not of causal interest.

Statistical Analysis of Statistical Data with the Boundary Intervals Derived From the Two-component Mean Model

A Manchure Library for the Semantic Image Tagging of Images

Neural Fisher Discriminant Analysis

Highly Scalable Latent Semantic Models

Large-Scale Automatic Analysis of Chessboard GamesGame mechanics and game theory, particularly those related to the game of chess, are often associated in non-linear causal structures and theories. In this paper, we present a probabilistic model for probabilistic causal structure representations of games, where games are simulated. We demonstrate that for some games, the model may be able to infer causal structures from random state values with an accuracy of near-optimal, considering that the causal structure is often not of causal interest.