Unsupervised learning
Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates.
... where unsupervised learning does occur (unlike in the situation considered in previous studies [7,16]) and so, even without reinforcement learning, a balance of LTP and LTD produces stable firing ...
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Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
... and Unsupervised learning algorithms in pattern classification scenario and to discuss the efficiency of these models in an education industry as a sample ...two learning algorithms are addressing ...
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Unsupervised learning of ontology for the medical domain
... In our approach, we use different techniques, like Asymmetric Association Mea- sures, clustering algorithm and text rank algorithm, so that we can obtain relations between a set of terms[r] ...
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Plant Leaf Disease Detection based on Unsupervised Learning
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6
Artificial Neural Network : A Brief Overview
... Unsupervised learning is a machine learning technique that sets parameters of an artificial neural network based on given data and a cost function which is to be ...formulation. Unsupervised ...
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An Arabic Text-To-Speech System Based on Artificial Neural Networks
... Abstract: Problem statement: With the rapid advancement in information technology and communications, computer systems increasingly offer the users the opportunity to interact with information through speech. The ...
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A Study on Selective Data Mining Algorithms
... of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining,pattern recognition, image analysis,information retrieval, ...
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An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm
... as unsupervised learning (in pattern recognition), numerical taxonomy (in biology, ecology), typology (in social sciences) and partition (in graph theory) ...
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Email Grouping Method
... implemented unsupervised learning techniques, and uses email content with vocabulary learning system to decide the email groupings and this applies to any email management ...
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An Unsupervised Approach for Mining Multiple Web Databases
... . The idea of unsupervised learning for duplicate detection has its roots in the probabilistic model proposed by Fellegi and Sunter. When there is no training data to compute the probability estimates, it ...
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A New Method of Learning: M-Learning (Mobile Learning)
... With the increase tremendous growth of mobile users new mobile companies are entered in the market with new technologies, mobile prices are going down. With the entrance of smart phones and android operating system the ...
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ESTILO DE APRENDIZAGEM COLABORATIVO PARA O E-LEARNING <em> COLLABORATIVE LEARNING STYLES FOR E-LEARNING </em>
... Collaborative learning is one of the core drivers of current education ...of learning. This study focuses on e-learning with the purpose of identifying actions that encourage and expand the use of ...
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A Model for Classification Secondary School Student Enrollment Approval Based on E-Learning Management System and E-Games
... identifying learning skills based on ...are learning in order to gather hints about their learning ...mechanism, learning skills are calculated based on the gathered ...identifying ...
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OPTIMIZATION OF WELD PENETRATION PROBLEM IN BUTT WELDED JOINTS USING STRESS CONCENTRATION FACTORS AND STRESS INTENSITY FACTORS
... Reinforcement Learning is learning from interactions with an environment, from the consequences of action, rather than from explicit ...Reinforcement Learning algorithms are methods for solving ...
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Sequential Extraction Thresholding Clustering for Segmentation of Coastal Upwelling on Sea Surface Temperature Images
... It is always very hard to find out a good index for the unsupervised analysis because we do not have any ground truth and it is a very challenging problem nowadays. In this study, we took most popular indices as ...
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Unsupervised Rehabilitation: effects of Exercise Training over the Long Run
... Methods - We carried out a retrospective study with 30 patients divided into: group I (GI) - 15 patients from private clinics undergoing unsupervised rehabilitation; group II (GII) - control group, 15 patients ...
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An unsupervised text mining method for relation extraction from biomedical literature.
... Evaluation on protein–protein interactions extraction We use AImed corpus as the benchmark dataset for protein– protein interactions extraction. AImed corpus is manually devel- oped by Bunescu et al. for protein–protein ...
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Acupuncture modulates resting state hippocampal functional connectivity in Alzheimer disease.
... Table 1. AVLT, Auditory verbal learning test; immediate, immediate recall of learning verbal; delayed; delayed recall of learning verbal; recognition, recognition of learning verbal; CDR[r] ...
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Intentional Learning Vs Incidental Learning
... intentional learning and successfully demonstrated that intentional learning is better than incidental ...Intentional learning instructions produce better recall and recognition performance than ...
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Psicol. Esc. Educ. vol.11 número especial
... of learning’ is representative not only of a shift in focus in higher education, but of a series of changes throughout the sector which can be described as producing a major systems reconfiguration (Ison, 1999, ...
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