Journal ArticleDOI
Pattern Recognition and Machine Learning
TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Journal ArticleDOI
Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders
Andre F. Marquand,Andre F. Marquand,Thomas Wolfers,Maarten Mennes,Jan K. Buitelaar,Christian F. Beckmann,Christian F. Beckmann +6 more
TL;DR: The predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders the authors review, but for most disorders it has not converged on a consistent set of subgroups.
Journal ArticleDOI
Laplacian Regularized Gaussian Mixture Model for Data Clustering
TL;DR: This paper introduces a regularized probabilistic model based on manifold structure for data clustering, called Laplacian regularized Gaussian Mixture Model (LapGMM), which is modeled by a nearest neighbor graph, and the graph structure is incorporated in the maximum likelihood objective function.
Journal ArticleDOI
IDNet: Smartphone-based gait recognition with convolutional neural networks
Matteo Gadaleta,Michele Rossi +1 more
TL;DR: IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recognition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework.
Proceedings Article
Exclusive Lasso for Multi-task Feature Selection
TL;DR: Experiments with document categorization show that the proposed exclusive lasso regularizer outperforms state-of-theart algorithms for multi-task feature selection and an efficient algorithm is derived to solve the related optimization problem.
Journal ArticleDOI
A systematic comparison of supervised classifiers.
Diego R. Amancio,Cesar H. Comin,Dalcimar Casanova,Gonzalo Travieso,Odemir Martinez Bruno,Francisco A. Rodrigues,Luciano da Fontoura Costa +6 more
TL;DR: The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM), and the k-nearest neighbor method frequently allowed the best accuracy.