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Displaying 1-50 out of 70 total
Lifted-Rollout for Approximate Policy Iteration of Markov Decision Process
Found in: Data Mining Workshops, International Conference on
By Wang-Zhou Dai,Yang Yu,Zhi-Hua Zhou
Issue Date:December 2011
pp. 689-696
Sampling-based approximate policy iteration, which samples (or
 
Using Neural Networks for Fault Diagnosis
Found in: Neural Networks, IEEE - INNS - ENNS International Joint Conference on
By Jia-Zhou He, Zhi-Hua Zhou, Xu-Ri Yin, Shi-Fu Chen
Issue Date:July 2000
pp. 5217
In this paper, a universal Fault Instance Model, which aims to solve problems existing in the present technology of fault diagnosis, such as the lack of universality, the difficulty in the use of real time system and the dilemma of stability and plasticity...
 
Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Jian-Sheng Wu,Sheng-Jun Huang,Zhi-Hua Zhou
Issue Date:September 2014
pp. 1-1
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multi-domain and multi-func...
 
Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Sheng-Jun Huang,Zhi-Hua Zhou
Issue Date:December 2013
pp. 1079-1084
In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes...
 
Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Xu-Ying Liu,Qian-Qian Li,Zhi-Hua Zhou
Issue Date:December 2013
pp. 478-487
Class-imbalance is very common in real data mining tasks. Previous studies focused on binary-class imbalance problem, whereas multi-class imbalance problem is more challenging. Error correcting output codes (ECOC) technique can be applied to class-imbalanc...
 
Efficient Optimization of Performance Measures by Classifier Adaptation
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Nan Li,Ivor W. Tsang,Zhi-Hua Zhou
Issue Date:June 2013
pp. 1370-1382
In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear cla...
 
Sequence-Based Prediction of microRNA-Binding Residues in Proteins Using Cost-Sensitive Laplacian Support Vector Machines
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Jian-Sheng Wu, Zhi-Hua Zhou
Issue Date:May 2013
pp. 752-759
The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training ...
 
Transductive Multilabel Learning via Label Set Propagation
Found in: IEEE Transactions on Knowledge and Data Engineering
By Xiangnan Kong,Michael K. Ng,Zhi-Hua Zhou
Issue Date:March 2013
pp. 704-719
The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image annota...
 
Exploitation of label relationship in multi-label learning
Found in: 2012 IEEE International Conference on Granular Computing (GrC-2012)
By Zhi-Hua Zhou
Issue Date:August 2012
pp. 19
Traditional supervised learning deals with problems where one instance is associated with a single class label, whereas in many real tasks, one instance may be associated with multiple class labels simultaneously; for example, an image can be tagged with s...
 
Unsupervised metric fusion by cross diffusion
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Bo Wang, Jiayan Jiang, Wei Wang, Zhi-Hua Zhou, Zhuowen Tu
Issue Date:June 2012
pp. 2997-3004
Metric learning is n fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary information. In this paper; we propose a fusion algorithm which outputs enhanced...
 
A Taxi Driving Fraud Detection System
Found in: Data Mining, IEEE International Conference on
By Yong Ge,Hui Xiong,Chuanren Liu,Zhi-Hua Zhou
Issue Date:December 2011
pp. 181-190
Advances in GPS tracking technology have enabled us to install GPS tracking devices in city taxis to collect a large amount of GPS traces under operational time constraints. These GPS traces provide unparallel opportunities for us to uncover taxi driving f...
 
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Ying-Xin Li,Shuiwang Ji,Sudhir Kumar,Jieping Ye,Zhi-Hua Zhou
Issue Date:January 2012
pp. 98-112
In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured i...
 
Exploiting Unlabeled Data to Enhance Ensemble Diversity
Found in: Data Mining, IEEE International Conference on
By Min-Ling Zhang, Zhi-Hua Zhou
Issue Date:December 2010
pp. 619-628
Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ...
 
When Does Cotraining Work in Real Data?
Found in: IEEE Transactions on Knowledge and Data Engineering
By Jun Du, Charles X. Ling, Zhi-Hua Zhou
Issue Date:May 2011
pp. 788-799
Cotraining, a paradigm of semisupervised learning, is promised to alleviate effectively the shortage of labeled examples in supervised learning. The standard two-view cotraining requires the data set to be described by two views of features, and previous s...
 
Social Learning
Found in: IEEE Intelligent Systems
By Qiang Yang, Zhi-Hua Zhou, Wenji Mao, Wei Li, Nathan Nan Liu
Issue Date:July 2010
pp. 9-11
In recent years, social behavioral data have been exponentially expanding due to the tremendous success of various outlets on the social Web (aka Web 2.0) such as Facebook, Digg, Twitter, Wikipedia, and Delicious. As a result, there's a need for social lea...
 
Semi-naive Exploitation of One-Dependence Estimators
Found in: Data Mining, IEEE International Conference on
By Nan Li, Yang Yu, Zhi-Hua Zhou
Issue Date:December 2009
pp. 278-287
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, an...
 
Least Square Incremental Linear Discriminant Analysis
Found in: Data Mining, IEEE International Conference on
By Li-Ping Liu, Yuan Jiang, Zhi-Hua Zhou
Issue Date:December 2009
pp. 298-306
Linear discriminant analysis (LDA) is a well-known dimension reduction approach, which projects high-dimensional data into a low-dimensional space with the best separation of different classes. In many tasks, the data accumulates over time, and thus increm...
 
Cost-Sensitive Face Recognition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Yin Zhang, Zhi-Hua Zhou
Issue Date:October 2010
pp. 1758-1769
Most traditional face recognition systems attempt to achieve a low recognition error rate, implicitly assuming that the losses of all misclassifications are the same. In this paper, we argue that this is far from a reasonable setting because, in almost all...
 
Enhanced Pictorial Structures for precise eye localization under incontrolled conditions
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Xiaoyang Tan, Fengyi Song, Zhi-Hua Zhou, Songcan Chen
Issue Date:June 2009
pp. 1621-1628
In this paper, we present an enhanced pictorial structure (PS) model for precise eye localization, a fundamental problem involved in many face processing tasks. PS is a computationally efficient framework for part-based object modelling. For face images ta...
 
Learning a distance metric from multi-instance multi-label data
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Rong Jin, Shijun Wang, Zhi-Hua Zhou
Issue Date:June 2009
pp. 896-902
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. An example application of MIML is visual object recognition in which each image...
 
TEFE: A Time-Efficient Approach to Feature Extraction
Found in: Data Mining, IEEE International Conference on
By Li-Ping Liu, Yang Yu, Yuan Jiang, Zhi-Hua Zhou
Issue Date:December 2008
pp. 423-432
With the rapid evolution of internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One...
 
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
Found in: Data Mining, IEEE International Conference on
By Min-Ling Zhang, Zhi-Hua Zhou
Issue Date:December 2008
pp. 688-697
Multi-instance multi-label learning (MIML) deals with the problem where each training example is associated with not only multiple instances but also multiple class labels. Previous MIML algorithms work by identifying its equivalence in degenerated version...
 
Isolation Forest
Found in: Data Mining, IEEE International Conference on
By Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
Issue Date:December 2008
pp. 413-422
Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explici...
 
Distributional Features for Text Categorization
Found in: IEEE Transactions on Knowledge and Data Engineering
By Xiao-Bing Xue, Zhi-Hua Zhou
Issue Date:March 2009
pp. 428-442
Text categorization is the task of assigning predefined categories to natural language text. With the widely used 'bag of words' representation, previous researches usually assign a word with values such that whether this word appears in the document conce...
 
Correction to
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Xin Geng, Zhi-Hua Zhou, Kate Smith-Miles
Issue Date:February 2008
pp. 368
No summary available.
 
Cocktail Ensemble for Regression
Found in: Data Mining, IEEE International Conference on
By Yang Yu, Zhi-Hua Zhou, Kai Ming Ting
Issue Date:October 2007
pp. 721-726
This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formally analyze the optimal linear combination of two base ensembles, which is the...
 
Semisupervised Regression with Cotraining-Style Algorithms
Found in: IEEE Transactions on Knowledge and Data Engineering
By Zhi-Hua Zhou, Ming Li
Issue Date:November 2007
pp. 1479-1493
The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available but labeled ones are fai...
 
Automatic Age Estimation Based on Facial Aging Patterns
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Xin Geng, Zhi-Hua Zhou, Kate Smith-Miles
Issue Date:December 2007
pp. 2234-2240
While recognition of most facial variations, such as identity, expression and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique character...
 
Exploratory Under-Sampling for Class-Imbalance Learning
Found in: Data Mining, IEEE International Conference on
By Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou
Issue Date:December 2006
pp. 965-969
Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class examples are ignored. We propose two algorithms to overcome the deficiency. EasyEn...
 
Query-Sensitive Similarity Measure for Content-Based Image Retrieval
Found in: Data Mining, IEEE International Conference on
By Zhi-Hua Zhou, Hong-Bin Dai
Issue Date:December 2006
pp. 1211-1215
Similarity measure is one of the keys of a high-performance content-based image retrieval (CBIR) system. Given a pair of images, existing similarity measures usually produce a static and constant similarity score. However, an image can usually be perceived...
 
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Found in: Data Mining, IEEE International Conference on
By Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
Issue Date:December 2006
pp. 1178-1182
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples are known. In this paper, we propose an adaptive kernel selection method for ke...
 
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
Found in: Data Mining, IEEE International Conference on
By Xu-Ying Liu, Zhi-Hua Zhou
Issue Date:December 2006
pp. 970-974
In real-world applications the number of examples in one class may overwhelm the other class, but the primary interest is usually on the minor class. Cost-sensitive learning has been deeded as a good solution to these class-imbalanced tasks, yet it is not ...
 
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
Found in: IEEE Transactions on Knowledge and Data Engineering
By Min-Ling Zhang, Zhi-Hua Zhou
Issue Date:October 2006
pp. 1338-1351
In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural ne...
 
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou
Issue Date:June 2006
pp. 138-145
The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works usually utilize metric distances which are ofen epistemologically different f...
 
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
Found in: IEEE Transactions on Knowledge and Data Engineering
By Zhi-Hua Zhou, Xu-Ying Liu
Issue Date:January 2006
pp. 63-77
This paper studies empirically the effect of sampling and threshold-moving in training cost-sensitive neural networks. Both oversampling and undersampling are considered. These techniques modify the distribution of the training data such that the costs of ...
 
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
Found in: IEEE Transactions on Knowledge and Data Engineering
By Zhi-Hua Zhou, Ming Li
Issue Date:November 2005
pp. 1529-1541
In many practical data mining applications, such as Web page classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attract...
 
NeC4.5: Neural Ensemble Based C4.5
Found in: IEEE Transactions on Knowledge and Data Engineering
By Zhi-Hua Zhou, Yuan Jiang
Issue Date:June 2004
pp. 770-773
<p><b>Abstract</b>—Decision tree is with good comprehensibility while neural network ensemble is with strong generalization ability. In this paper, these merits are integrated into a novel decision tree algorithm NeC4.5. This algorithm tr...
 
A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques
Found in: Tools with Artificial Intelligence, IEEE International Conference on
By Zhi-Hua Zhou, Min-Ling Zhang, Ke-Jia Chen
Issue Date:November 2003
pp. 565
In multi-instance learning, the training examples are bags composed of instances without labels and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. In content-based image retrieval (CBIR), the query i...
 
A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks
Found in: Neural Networks, IEEE - INNS - ENNS International Joint Conference on
By Zhi-Hua Zhou, Shi-Fu Chen, Zhao-Qian Chen
Issue Date:July 2000
pp. 3401
In this paper, a statistics based approach named STARE that is designed to extract symbolic rules from trained neural networks is proposed. STARE deals with continuous attributes in a unique way so that not only different attributes could be discretized to...
 
A Review on Multi-Label Learning Algorithms
Found in: IEEE Transactions on Knowledge and Data Engineering
By Min-Ling Zhang,Zhi-Hua Zhou
Issue Date:August 2014
pp. 1-1
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learn...
 
ArchRanker: A ranking approach to design space exploration
Found in: 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA)
By Tianshi Chen,Qi Guo,Ke Tang,Olivier Temam,Zhiwei Xu,Zhi-Hua Zhou,Yunji Chen
Issue Date:June 2014
pp. 85-96
Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts arc...
   
Active Learning by Querying Informative and Representative Examples
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Sheng-Jun Huang,Rong Jin,Zhi-Hua Zhou
Issue Date:February 2014
pp. 1
Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select...
 
Towards Making Unlabeled Data Never Hurt
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Yu-Feng Li,Zhi-Hua Zhou
Issue Date:February 2014
pp. 1
It is usually expected that learning performance can be improved by exploiting unlabeled data, particularly when the number of labeled data is limited. However, it has been reported that, in some cases existing semi-supervised learning approaches perform e...
 
Facial Age Estimation by Learning from Label Distributions
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Xin Geng, Chao Yin, Zhi-Hua Zhou
Issue Date:October 2013
pp. 2401-2412
One of the main difficulties in facial age estimation is that the learning algorithms cannot expect sufficient and complete training data. Fortunately, the faces at close ages look quite similar since aging is a slow and smooth process. Inspired by this ob...
 
B-Planner: Night bus route planning using large-scale taxi GPS traces
Found in: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom)
By Chao Chen,Daqing Zhang,Zhi-Hua Zhou,Nan Li,Tulin Atmaca,Shijian Li
Issue Date:March 2013
pp. 225-233
Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning i...
   
Cost-sensitive face recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Yin Zhang, Zhi-Hua Zhou
Issue Date:June 2008
pp. 1-8
Traditional face recognition systems attempt to achieve a high recognition accuracy, which implicitly assumes that the losses of all misclassifications are the same. However, in many real-world tasks this assumption is not always reasonable. For example, i...
 
Effective and efficient microprocessor design space exploration using unlabeled design configurations
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By Ling Li, Qi Guo, Tianshi Chen, Yunji Chen, Zhi-Hua Zhou, Zhiwei Xu
Issue Date:December 2013
pp. 1-18
Ever-increasing design complexity and advances of technology impose great challenges on the design of modern microprocessors. One such challenge is to determine promising microprocessor configurations to meet specific design constraints, which is called De...
     
Sequence-Based Prediction of microRNA-Binding Residues in Proteins Using Cost-Sensitive Laplacian Support Vector Machines
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Jian-Sheng Wu, Zhi-Hua Zhou
Issue Date:May 2013
pp. 752-759
The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training ...
     
Multi-label hypothesis reuse
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Sheng-Jun Huang, Yang Yu, Zhi-Hua Zhou
Issue Date:August 2012
pp. 525-533
Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts. It is well-accepted that, in order to achieve a good performance, the relationship among labels should be exploited. Most existing approach...
     
Introduction to the Special Section on Distance Metric Learning in Intelligent Systems
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By Jinhui Tang, Rong Jin, Steven C. H. Hoi, Zhi-Hua Zhou
Issue Date:May 2012
pp. 1-2
Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data ...
     
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