Z
Zhi-Hua Zhou
Researcher at Nanjing University
Publications - 633
Citations - 64307
Zhi-Hua Zhou is an academic researcher from Nanjing University. The author has contributed to research in topics: Semi-supervised learning & Artificial neural network. The author has an hindex of 102, co-authored 626 publications receiving 52850 citations. Previous affiliations of Zhi-Hua Zhou include Michigan State University & Tokyo Institute of Technology.
Papers
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Proceedings Article
Improve web search using image snippets
TL;DR: This paper shows that the Web search performance can be enhanced if image information is considered and a new Web search framework is proposed, where image snippets are extracted for the Web pages, which are then provided along with text snippets to the user such that it is much easier and more accurate for the user to identify the Webpages he or she expects and to reformulate the initial query.
Proceedings Article
A new approach to estimating the expected first hitting time of evolutionary algorithms
Yang Yu,Zhi-Hua Zhou +1 more
TL;DR: In this paper, a new approach to estimate the expected first hitting time is proposed by exploiting the relationship between the convergence rate and the expected hitting time, which is then applied to four evolutionary algorithms which involve operators of mutation with population, mutation with recombination, and time-variant mutation.
Book ChapterDOI
Generation of comprehensible hypotheses from gene expression data
Yuan Jiang,Ming Li,Zhi-Hua Zhou +2 more
TL;DR: A general approach to generate comprehensible hypotheses from gene expression data is described and applied to human acute leukemias as a test case, demonstrating the feasibility of using machine learning techniques to help form hypotheses on the relationship between genes and certain diseases.
Book ChapterDOI
Dependency bagging
TL;DR: A new variant of Bagging named DepenBag is proposed that obtains bootstrap samples at first and employs a causal discoverer to induce from each sample a dependency model expressed as a Directed Acyclic Graph (DAG).
Proceedings Article
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
TL;DR: A new recipe is proposed to corral a larger band of bandit algorithms whose regret overhead has only logarithmic dependence on M as long as some conditions are satisfied, which extends to linear bandits over a smooth and strongly convex domain as well as unconstrained linear bandits.