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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Journal ArticleDOI
Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies.
TL;DR: Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort, and enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.
Proceedings Article
Bayesian Optimization with Inequality Constraints
TL;DR: This work presents constrained Bayesian optimization, which places a prior distribution on both the objective and the constraint functions, and evaluates this method on simulated and real data, demonstrating that constrainedBayesian optimization can quickly find optimal and feasible points, even when small feasible regions cause standard methods to fail.
Journal ArticleDOI
A Review of Domain Adaptation without Target Labels
Wouter M. Kouw,Marco Loog +1 more
TL;DR: In this paper, the authors present a categorization of domain adaptation methods into three types: sample-based, feature-based and inference-based methods, based on which a classifier learns from a source domain and generalizes to a target domain.
Journal ArticleDOI
Network anomaly detection with the restricted Boltzmann machine
TL;DR: The effectiveness of a detection approach based on machine learning is explored, using the Discriminative Restricted Boltzmann Machine to combine the expressive power of generative models with good classification accuracy capabilities to infer part of its knowledge from incomplete training data.
Proceedings ArticleDOI
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction
TL;DR: A prototype system and a prototype system are developed that show that CS2P outperforms state-of-art by 40% and 50% median pre- diction error respectively for initial and midstream through- put and improves QoE by 14% over buffer-based adaptation algorithm.