The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games...

In this paper a new method for dimensionality reduction in machine learning is proposed and called as Lie group Machine Learning (LML). The theory framework of LML is given, including the conception of one-parameter subgroup, Lie algebra and LML; the geometric properties of LML; the generalization hypothesis axiom, the partition independence hypothesis axiom, the duality hypothesis axiom, the l...

Abstract Funding Acknowledgements Type of funding sources: None. Background The excess mortality in hypertrophic cardiomyopathy (HCM) patients is mainly attributed to the occurrence sudden cardiac death (SCD). prediction ventricular arrhythmias remains challenging and could be improved. Purpose This study evaluated added predictive value a machine learning-based model combining clinical convent...

In this paper we explore synergies between the machine learning and knowledge representation fields by considering how scientific knowledge is represented in these areas. We illustrate some of the knowledge obtained through machine learning methods, providing two contrasting examples of such models: probabilistic graphical models (aka Bayesian networks) and artificial neural networks (including...

In this paper we present experiments for the detection of terrorism incident types from news summary. The news summaries from the global terrorism dataset have been analyzed using machine learning techniques. We have conducted experiments using different learning algorithms including Naive Bayes, decision tree and support vector machine. The results of the experiments show that decision tree le...

background and objectives: arteriovenous fistula is a popular vascular access method for surgical treatmentof hemodialysis patients. the method, however, is associated with a high rate of early failure varying in the range of 20-60%. predicting early arteriovenous fistula failure and its risk factors can help reduce its incidence, its hospitalization rate, and associated costs. in this study, w...

Machine learning has been well applied and recognized as an effective tool to handle a wide range of real situations, including medical applications. In this scenario, it can help to alleviate problems typically suffered by researchers in this field, such as saving time for practitioners and providing unbiased results. This tutorial is concerned with the use of machine learning techniques to so...

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has fol...

Automatic differentiation—the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately—dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learning. Despite this, practitioners in a variety of fields, including machine learnin...