Now showing items 1-10 of 1698
The assessment of the quality of sugar using electronic tongue and machine learning algorithms
The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system ...
LEARNING SPAM FEATURES USING RESTRICTED BOLTZMANN MACHINES
Nowadays, spam detection has been one of the foremost machine learning-oriented applications in the context of security in computer networks. In this work, we propose to learn intrinsic properties of e-mail messages by ...
Temperature-Based Deep Boltzmann Machines
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-based user identification. Despite other ...
Preliminary diagnosis of ophtalmological diseases through machine learning techniques
Although one can find several patents addressing surgery procedures to tackle ophthalmological diseases, it is very unusual to find other ones that apply machine learning techniques to automatically identify them. In this ...
Meta-heuristic multi- and many-objective optimization techniques for solution of machine learning problems
Recently, multi- and many-objective meta-heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a ...
Uma Introdução às Support Vector Machines
(Instituto de Informática - Universidade Federal do Rio Grande do Sul, 2007)
Speeding-up reinforcement learning through abstraction and transfer learning
(Saint Paul, Minnesota, 2013-05-10)
We are interested in the following general question: is it pos- sible to abstract knowledge that is generated while learning the solution of a problem, so that this abstraction can ac- celerate the learning process? ...
A fast hybrid reinforcement learning framework with human corrective feedback
Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning ...
The impact of commuting time over educational achievement: A machine learning approach
(Universidad de Chile. Facultad de Economía y Negocios, 2018)
Taking advantage of georeferenced data from Chilean students, we estimate the impact of commuting time over academic achievement. As the commuting time is an endogenous variable, we use instrumental variables and fixed ...