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
Journal ArticleDOI
iVisClustering: An Interactive Visual Document Clustering via Topic Modeling
TL;DR: An interactive visual analytics system for document clustering, called iVisClustering, is proposed based on a widely‐used topic modeling method, latent Dirichlet allocation (LDA), which provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates.
Proceedings ArticleDOI
Overlapping community detection via bounded nonnegative matrix tri-factorization
Yu Zhang,Dit-Yan Yeung +1 more
TL;DR: This paper proposes a method called bounded nonnegative matrix tri-factorization (BNMTF), which can explicitly model and learn the community membership of each node as well as the interaction among communities using three factors in the factorization.
A Divergence Minimization Perspective on Imitation Learning Methods
TL;DR: A unified probabilistic perspective on IL algorithms based on divergence minimization is presented, conclusively identifying that IRL's state-marginal matching objective contributes most to its superior performance, and applies the new understanding of IL methods to the problem of state-Marginal matching.
Proceedings Article
Using Universal Linguistic Knowledge to Guide Grammar Induction
TL;DR: This work presents an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages and outperforms state-of-the-art unsupervised methods by a significant margin.
Journal ArticleDOI
Human-assisted graph search: it's okay to ask questions
TL;DR: This work provides the first formal algorithmic study of the optimization of human computation for graph search by asking an omniscient human questions of the form "Is there a target node that is reachable from the current node?".