53 Result(s)
within
**n Hua Zhou
Computer Science
Algorithm Analysis and Problem Complexity
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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 ...
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Chapter and Conference Paper
Ensembles of Multi-instance Learners
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing two famous multi-instance lear...
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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
Exploiting Unlabeled Data in Content-Based Image Retrieval
In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (Cbir), is proposed. This approach ...
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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...
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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
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
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
Distributional Features for Text Categorization
In previous research of text categorization, a word is usually described by features which express that whether the word appears in the document or how frequently the word appears. Although these features are ...
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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
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 ...
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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
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
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...
