Titlebook: Ensembles in Machine Learning Applications; Oleg Okun,Giorgio Valentini,Matteo Re Book 2011 Springer Berlin Heidelberg 2011 Computational

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A Novel Ensemble Technique for Protein Subcellular Location Prediction,rect Acyclic Graph (.). Each base classifier, called ., is mainly based on the projection of the given points on the Fisher subspace, estimated on the training data, by means of a novel technique. The proposed multiclass classifier is applied to the task of protein subcellular location prediction, w

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An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes,y partitions the input space of a problem into a number of subspaces, experts becoming specialized on each subspace. To manage this process, theME uses an expert called gating network, which is trained together with the other experts. In this chapter, we propose a modified version of the ME algorith

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Three Data Partitioning Strategies for Building Local Classifiers,udy we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training. The first two strategies are based on clustering the training data and building an individual classifier for each cluster or a combination. The third

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https://doi.org/10.1007/978-3-642-22910-7Computational Intelligence; Computational Intelligence; Ensembles in Machine Learning Applications; Ens

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978-3-662-50706-3Springer Berlin Heidelberg 2011

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https://doi.org/10.1007/978-94-007-6683-9s (AUs). The method adopted is to train a single Error-Correcting Output Code (ECOC) multiclass classifier to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to calibrate the ECOC outputs to probabilities and appro