33 Result(s)
within
Conference Paper
**n Hua Zhou
Computer Science
Algorithm Analysis and Problem Complexity
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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
Text Relevance Analysis Method over Large-Scale High-Dimensional Text Data Processing
As the amount of digital information is exploding in social, industry and scientific areas, MapReduce is a distributed computation framework, which has become widely adopted for analytics on large-scale data. ...
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Chapter and Conference Paper
A PWF Smoothing Algorithm for K-Sensitive Stream Mining Technologies over Sliding Windows
The development of Streaming Mining technologies as a hotspot entered the limelight, which is more effectively to avoid big data and distributed streams mining problems. Especially for the IoT and Ubiquitous Comp...
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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
Quantitative Model of Personnel Allocation Based on Information Entropy
In the field of the software project management, the distribution and organization of developers has always been a research focus. In a software project, it is of great importance to divide the modules and per...
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Chapter and Conference Paper
Stability of a Predator-Prey Model with Modified Holling-Type II Functional Response
A predator-prey model with modified Holling-Type II functional response under Neumann boundary condition is proposed. We show that under some conditions the cross-diffusion can induce the Turing instability of...
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Chapter and Conference Paper
Using Multiple Objective Functions in the Dynamic Model of Metabolic Networks of Escherichia coli
Different objective functions in the dynamic model can explore the diverse properties of the solution space, and a wide variety of capabilities of an organism. In that way, when there is a fact that several co...
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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
Building Decision Trees for the Multi-class Imbalance Problem
Learning in imbalanced datasets is a pervasive problem prevalent in a wide variety of real-world applications. In imbalanced datasets, the class of interest is generally a small fraction of the total instances...
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Chapter and Conference Paper
Spectral Analysis of k-Balanced Signed Graphs
Previous studies on social networks are often focused on networks with only positive relations between individual nodes. As a significant extension, we conduct the spectral analysis on graphs with both positiv...
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Chapter and Conference Paper
Cost-Sensitive Learning
In conventional classification settings, the classifiers generally try to maximize the accuracy or minimize the error rate, both are equivalent to minimizing the number of mistakes in classifying new instances. S...
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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
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data
Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational...
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Chapter and Conference Paper
Analyzing Co-training Style Algorithms
Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present ...
