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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Proceedings ArticleDOI
Field-aware Factorization Machines for CTR Prediction
TL;DR: This paper establishes FFMs as an effective method for classifying large sparse data including those from CTR prediction, and proposes efficient implementations for training FFMs and comprehensively analyze FFMs.
Proceedings ArticleDOI
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
TL;DR: A novel Expectation-Maximization (EM) based method is formulated that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches and applies it to the classification of glioma and non-small-cell lung carcinoma cases into subtypes.
Journal ArticleDOI
Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
TL;DR: In this article, a statistical machine learning framework was developed to study the effect of eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, etc.) on two output variables, namely heating load (HL) and cooling load (CL), of residential buildings.
Adaptive Networks
TL;DR: Under reasonable technical conditions on the data, the adaptive networks are shown to be mean square stable in the slow adaptation regime, and their mean square error performance and convergence rate are characterized in terms of the network topology and data statistical moments.
Journal ArticleDOI
Fairness in Criminal Justice Risk Assessments: The State of the Art
TL;DR: In this paper, a discussion of fairness in criminal justice risk assessments typically lacks conceptual precision. Rhetoric too often substitutes for careful analysis, and the authors seek to clarify this issue.