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Learning with Drift Detection
TLDR
A method for detection of changes in the probability distribution of examples, to control the online error-rate of the algorithm and to observe that the method is independent of the learning algorithm. Expand
Learning with Local Drift Detection
TLDR
This work presents a method for detection of changes in the probability distribution of examples to monitor the online error-rate of a learning algorithm looking for significant deviations, and presents experiments using the method as a wrapper over a decision tree and a linear model, and in each internal-node of a decision Tree. Expand
Using Bayesian networks to improve knowledge assessment
TLDR
The integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE) of the University of Aveiro and results show a high degree of agreement among the scores given by the experts and also among the diagnosis provided by the BSM in the written exam and expert's average. Expand
An Adaptive Predictive Model for Student Modeling
TLDR
A probabilistic adaptive predictive model is proposed that includes a method to handle concept drift based on Statistical Quality Control and should be successfully used in similar user modeling prediction tasks, where uncertainty and concept drift are presented. Expand
Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles
TLDR
A student model to account for learning styles is presented, based on the model defined by Felder and Sylverman and implemented using dynamic Bayesian networks. Expand
A comparative study on feature selection and adaptive strategies for email foldering using the ABC-DynF framework
TLDR
The main aim is to study how feature ranking methods, concept drift monitoring, adaptive strategies and the implementation of a dynamic feature space can affect the performance of Bayesian email classification systems. Expand
Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams
TLDR
It is concluded that the Support Vector Machines (SVM) technique has higher accuracy than the k-Nearest Neighbor, Neural Networks and Kernel Naïve Bayes in terms of adaptation to a new kind of data. Expand
A Methodology for Developing Adaptive Educational-Game Environments
TLDR
A methodology for describing adaptive educational-game environments and a model that supports the environment design process that allows the specification of educational methods that can be used for the game environment generation. Expand
Discovering Student Preferences in E-Learning
TLDR
This paper uses all the background knowledge available about a particular student to build an initial decision model based on learning styles, which can be fine-tuned with the data generated by the student's interactions with the system in order to reflect more accurately his/her current preferences. Expand
Adaptive Bayes
TLDR
This paper presents Adaptive Bayes, an extension to the well-known naive-Bayes, one of the most common used learning algorithms for the task of user modeling, and shows significant advantages in comparison against their non-adaptive versions. Expand
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