Journal ArticleDOI
Pattern Recognition and Machine Learning
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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
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Journal ArticleDOI
Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping
TL;DR: This paper aims for fuzzification of continuous spatial data used as proxy evidence to facilitate and to support fuzzy MPM to generate exploration target areas for further examination of undiscovered deposits and proposes to adapt the concept of expected value to improve fuzzy logic MPM.
Journal ArticleDOI
Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors
TL;DR: In this paper, a machine learning approach for interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided.
Journal ArticleDOI
Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks
TL;DR: This paper combines deep reinforcement learning with a novel neural network structure based on graph convolutional networks, and proposes a new and efficient algorithm for automatic virtual network embedding that achieves best performance on most metrics compared with the existing state-of-the-art solutions.
Journal ArticleDOI
Application of artificial intelligence in gastroenterology
Young Joo Yang,Chang Seok Bang +1 more
TL;DR: Outside validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory and interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance.
Book
Machine Learning for Text
TL;DR: This textbook covers machine learning topics for text in detail and targets graduate students in computer science, as well as researchers, professors, and industrialpractitioners working in these related fields.