Structural Correspondence Analysis for Semi-supervised Learning – Most current methods treat a set of discrete observations (e.g., a model and a test) as a collection of observations. Such approaches typically assume that samples are modeled as discrete samples, which may not be the case. In this work we present a new approach for classification experiments based on Bayesian networks, where the classifier is a single distribution over observations. In addition, we present a generalization error measure that enables us to compare the resulting classifiers to a subset of the observed distributions. To the best of our knowledge, our contribution is the first one to analyze data in this manner, outperforming a state-of-the-art classification algorithm in this task.
The problem of predicting the future, for players of any given game, is commonly approached as a multi-agent game. This novel approach proposes a novel approach and an improvement is proposed in the form of a new algorithm, which is a modified version of the classical multi-agent game with different players. It is shown that the new algorithm performs better than the classical approach.
Neural Speech Recognition Using the NaCl Convolutional Neural Network
Compositional Argumentation with Inter-rater Agreement
Structural Correspondence Analysis for Semi-supervised Learning
A Multi-Task Algorithm for Predicting Player Profiles and their Predictions from Social MediaThe problem of predicting the future, for players of any given game, is commonly approached as a multi-agent game. This novel approach proposes a novel approach and an improvement is proposed in the form of a new algorithm, which is a modified version of the classical multi-agent game with different players. It is shown that the new algorithm performs better than the classical approach.