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Journal ArticleDOI

Pattern Recognition and Machine Learning

Radford M. Neal
- 01 Aug 2007 - 
- Vol. 49, Iss: 3, pp 366-366
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.

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Signal Processing for Music Analysis

TL;DR: It is demonstrated that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.
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Revealing Household Characteristics from Smart Meter Data

TL;DR: A system that uses supervised machine learning techniques to automatically estimate specific “characteristics” of a household from its electricity consumption, which paves the way for targeted energy efficiency programs and other services that benefit from improved customer insights is developed.
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Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

TL;DR: Gaussian process (GP) classification is introduced and it is shown that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel).
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Design, implementation and validation of a novel open framework for agile development of mobile health applications.

TL;DR: mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of mHealth and biomedical apps, and implements several functionalities to support resource and communication abstraction.
Proceedings ArticleDOI

3D Pictorial Structures for Multiple Human Pose Estimation

TL;DR: A novel 3D pictorial structures (3DPS) model is introduced that infers 3D human body configurations from the authors' reduced state space and is generic and applicable to both single and multiple human pose estimation.