12 Result(s)
within
**n Hua Zhou
Computer Science
Algorithm Analysis and Problem Complexity
Pattern Recognition
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Chapter and Conference Paper
A Lower Bound Analysis of Population-Based Evolutionary Algorithms for Pseudo-Boolean Functions
Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in real-world optimization tasks. However, previous theoretical studies often empl...
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Chapter and Conference Paper
Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective Optimization
Selection hyper-heuristics are automated methodologies for selecting existing low-level heuristics to solve hard computational problems. They have been found very useful for evolutionary algorithms when solvin...
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Chapter and Conference Paper
On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments
Sampling has been often employed by evolutionary algorithms to cope with noise when solving noisy real-world optimization problems. It can improve the estimation accuracy by averaging over a number of samples,...
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Chapter and Conference Paper
On Algorithm-Dependent Boundary Case Identification for Problem Classes
Running time analysis of metaheuristic search algorithms has attracted a lot of attention. When studying a metaheuristic algorithm over a problem class, a natural question is what are the easiest and the harde...
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Chapter and Conference Paper
Unlabeled Data and Multiple Views
In many real-world applications there are usually abundant unlabeled data but the amount of labeled training examples are often limited, since labeling the data requires extensive human effort and expertise. T...
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Chapter and Conference Paper
Towards Analyzing Recombination Operators in Evolutionary Search
Recombination (also called crossover) operators are widely used in EAs to generate offspring solutions. Although the usefulness of recombination has been well recognized, theoretical analysis on recombination ope...
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Chapter and Conference Paper
Multi-information Ensemble Diversity
Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from the information theoretic perspective,...
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Chapter and Conference Paper
A Prototype of Multimedia Metadata Management System for Supporting the Integration of Heterogeneous Sources
With the advances in information technology, the amount of multimedia metadata captured, produced, and stored is increasing rapidly. As a consequence, multimedia content is widely used for many applications in...
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Chapter and Conference Paper
Single Image Subspace for Face Recognition
Small sample size and severe facial variation are two challenging problems for face recognition. In this paper, we propose the SIS (Single Image Subspace) approach to address these two problems. To deal with t...
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Chapter and Conference Paper
Ensemble-Based Discriminant Manifold Learning for Face Recognition
The locally linear embedding (LLE) algorithm can be used to discover a low-dimensional subspace from face manifolds. However, it does not mean that a good accuracy can be obtained when classifiers work under t...
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Chapter and Conference Paper
Two-Dimensional Non-negative Matrix Factorization for Face Representation and Recognition
Non-negative matrix factorization (NMF) is a recently developed method for finding parts-based representation of non-negative data such as face images. Although it has successfully been applied in several appl...
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Chapter and Conference Paper
The Application of Visualization and Neural Network Techniques in a Power Transformer Condition Monitoring System
In this paper, visualization and neural network techniques are applied together to a power transformer condition monitoring system. Through visualizing the data from the chromatogram of oil-dissolved gases by ...
