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Improving the Accuracy of the Optimum-Path Forest Supervised Classifier for Large Datasets
(Springer, 2010-01-01)
In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the ...
Bayesian network classifiers: Beyond classification accuracy
(IOS PRESS, 2011)
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the ...
Approaches based on tree-structures classifiers to protein fold prediction
(Institute of Electrical and Electronics Engineers Inc., 2017-08)
Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of ...
Infrared face recogniton by optimum-path forest
(2009-12-01)
This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce ...
Infrared face recogniton by optimum-path forest
(2009-12-01)
This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce ...
Using anticipative hybrid extreme rotation forest to predict emergency service readmission risk
(Elsevier, 2017)
This paper provides a real life application of the recently published Anticipative Hybrid Extreme RotationForest (AHERF), which is an heterogeneous ensemble classifier that anticipates the correct fraction ofinstances from ...
Nearest Neighbors Distance Ratio Open-set Classifier
(SpringerDordrecht, 2017)
A new boosting design of Support Vector Machine classifiers
(Elsevier, 2015)
Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly ...
Boosted lazy associative classifier
(Universidade Federal de Minas GeraisUFMG, 2017-11-14)
Lazy machine learning algorithms have to learn every time it is been given a new example, however knowing which example is being classified gives them the advantage of adjusting their knowledge search accordingly. The Lazy ...