13 Result(s)
within
Chapter
**n Hua Zhou
Computer Science
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Chapter and Conference Paper
A Disability-Oriented Analysis Procedure for Leisure Rehabilitation Product Design
The leisure activities of current disabled people are primary static types rather than dynamic types. However, most marketed leisure exercise products seldom consider the requirements of the disabled people es...
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Chapter and Conference Paper
Large Margin Distribution Learning
Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the...
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Chapter and Conference Paper
Diversity Regularized Ensemble Pruning
Diversity among individual classifiers is recognized to play a key role in ensemble, however, few theoretical properties are known for classification. In this paper, by focusing on the popular ensemble pruning...
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Chapter and Conference Paper
On Detecting Clustered Anomalies Using SCiForest
Detecting local clustered anomalies is an intricate problem for many existing anomaly detection methods. Distance-based and density-based methods are inherently restricted by their basic assumptions—anomalies ...
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Chapter and Conference Paper
A Framework for Machine Learning with Ambiguous Objects
Machine learning tries to improve the performance of the system automatically by learning from experiences, e.g., objects or events given to the system as training samples. Generally, each object is represente...
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Chapter and Conference Paper
A Convex Method for Locating Regions of Interest with Multi-instance Learning
In content-based image retrieval (CBIR) and image screening, it is often desirable to locate the regions of interest (ROI) in the images automatically. This can be accomplished with multi-instance learning tec...
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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
Recognizing Face or Object from a Single Image: Linear vs. Kernel Methods on 2D Patterns
We consider the problem of recognizing face or object when only single training image per class is available, which is typically encountered in law enforcement, passport or identification card verification, et...
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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
Ensembles of Multi-Instance Neural Networks
Recently, multi-instance classification algorithm BP-MIP and multi-instance regression algorithm BP-MIR both based on neural networks have been proposed. In this paper, neural network ensemble techniques are i...
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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
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...
