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
More filters
Book
The Fourth Paradigm: Data-Intensive Scientific Discovery
TL;DR: This presentation will set out the eScience agenda by explaining the current scientific data deluge and the case for a “Fourth Paradigm” for scientific exploration.
Journal ArticleDOI
Millimeter Wave Channel Modeling and Cellular Capacity Evaluation
Mustafa Riza Akdeniz,Yuanpeng Liu,Mathew K. Samimi,Shu Sun,Sundeep Rangan,Theodore S. Rappaport,Elza Erkip +6 more
TL;DR: Detailed spatial statistical models of the channels are derived and it is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing.
Journal ArticleDOI
Evaluating Color Descriptors for Object and Scene Recognition
TL;DR: From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition and the usefulness of invariance is category-specific.
Journal ArticleDOI
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
TL;DR: A survey of automated text analysis for political science can be found in this article, where the authors provide guidance on how to validate the output of the models and clarify misconceptions and errors in the literature.
Journal ArticleDOI
Machine learning applications in cancer prognosis and prediction.
Konstantina Kourou,Themis P. Exarchos,Konstantinos P. Exarchos,Michalis V. Karamouzis,Dimitrios I. Fotiadis +4 more
TL;DR: Given the growing trend on the application of ML methods in cancer research, this work presents here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.