Journal ArticleDOI
Pattern Recognition and Machine Learning
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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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Book ChapterDOI
Local higher-order statistics (LHS) for texture categorization and facial analysis
TL;DR: This paper proposes a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features, which consistently achieves state-of-the-art performance on challenging texture and facialAnalysis datasets outperforming contemporary methods.
Journal ArticleDOI
Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning
TL;DR: The mathematical foundations of machine learning applications in fMRI are described, and two methods are focused on, support vector machines and relevance vector machines, respectively suited for the classification and regression of fMRI patterns.
Journal ArticleDOI
Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification
Hiroshi Higashi,Toshihisa Tanaka +1 more
TL;DR: DFBCSP is proposed that designs finite impulse response filters and the associated spatial weights by optimizing an objective function which is a natural extension of that of CSP and can effectively extract discriminative features for MI-BCI.
Proceedings ArticleDOI
Efficient learning of sparse, distributed, convolutional feature representations for object recognition
TL;DR: This is the first work showing that RBMs can be trained with almost no hyperparameter tuning to provide classification performance similar to or significantly better than mixture models (e.g., Gaussian mixture models).
Journal ArticleDOI
On the selection of appropriate distances for gene expression data clustering
TL;DR: This work analyzes how different distances and clustering methods interact regarding their ability to cluster gene expression data, i.e., microarray data, and supports that the selection of an appropriate distance depends on the scenario in hand.