16 Result(s)
within
**n Hua Zhou
Computer Science
Algorithm Analysis and Problem Complexity
Computation by Abstract Devices
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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
Magnetic Field Extrapolation Based on Improved Back Propagation Neural Network
Magnetic anomaly created by ferromagnetic ships may make them vulnerable to detections and mines. In order to reduce the anomaly, it is important to evaluate magnetic field firstly. Underwater field can be mea...
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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
Approximation Stability and Boosting
Stability has been explored to study the performance of learning algorithms in recent years and it has been shown that stability is sufficient for generalization and is sufficient and necessary for consistency...
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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
Selection of Optimal Technological Innovation Projects Combining Value Engineering with Fuzzy Synthetic Evaluation
Value engineering is introduced into a selection of optimal technological innovation projects. The function and cost factors of a project have been analyzed from the viewpoint of the whole enterprise, and new ...
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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
Method of Risk Discernment in Technological Innovation Based on Path Graph and Variable Weight Fuzzy Synthetic Evaluation
Risk in technological innovation is one of the important factors that hold enterprises from launching technological innovation. What cause the technological innovation risks is very complicated, and traditiona...
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Chapter and Conference Paper
Editing Training Data for kNN Classifiers with Neural Network Ensemble
Since kNN classifiers are sensitive to outliers and noise contained in the training data set, many approaches have been proposed to edit the training data so that the performance of the classifiers can be improve...
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Chapter and Conference Paper
Fuzzy-Kernel Learning Vector Quantization
This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functio...
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Chapter and Conference Paper
Robust Face Recognition from a Single Training Image per Person with Kernel-Based SOM-Face
In this paper, a kernel-based SOM-face method is proposed to recognize expression variant faces under the situation of only one training image per person. Based on the localization of the face, an unsupervised...
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Chapter and Conference Paper
Progressive Principal Component Analysis
Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best reconstruction for an original data in t...
