4.5 Article

Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning

Salvador Garcia et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2013)

Article Computer Science, Artificial Intelligence

A novel semi-supervised learning framework with simultaneous text representing

Yan Zhu et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2013)

Article Computer Science, Artificial Intelligence

A hybrid generative/discriminative method for semi-supervised classification

Zhen Jiang et al.

KNOWLEDGE-BASED SYSTEMS (2013)

Review Computer Science, Artificial Intelligence

A survey of multi-view machine learning

Shiliang Sun

NEURAL COMPUTING & APPLICATIONS (2013)

Article Computer Science, Artificial Intelligence

Effective semi-supervised document clustering via active learning with instance-level constraints

Weizhong Zhao et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2012)

Article Computer Science, Artificial Intelligence

A multiple kernel framework for inductive semi-supervised SVM learning

Xilan Tian et al.

NEUROCOMPUTING (2012)

Article Computer Science, Artificial Intelligence

Selective sampling techniques for feedback-based data retrieval

Hwanjo Yu

DATA MINING AND KNOWLEDGE DISCOVERY (2011)

Article Computer Science, Artificial Intelligence

When Does Cotraining Work in Real Data?

Jun Du et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2011)

Article Computer Science, Artificial Intelligence

Toward the Optimization of Normalized Graph Laplacian

Bo Xie et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)

Article Computer Science, Artificial Intelligence

Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions

Ke Chen et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2011)

Article Computer Science, Artificial Intelligence

ROBUST CO-TRAINING

Shiliang Sun et al.

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (2011)

Article Computer Science, Artificial Intelligence

A new co-training-style random forest for computer aided diagnosis

Chao Deng et al.

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2011)

Article Computer Science, Artificial Intelligence

Semi-supervised Bayesian ARTMAP

Xiao-liang Tang et al.

APPLIED INTELLIGENCE (2010)

Article Computer Science, Hardware & Architecture

Combining Committee-Based Semi-Supervised Learning and Active Learning

Mohamed Farouk Abdel Hady et al.

JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2010)

Article Computer Science, Artificial Intelligence

Semi-supervised learning by disagreement

Zhi-Hua Zhou et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2010)

Article Computer Science, Artificial Intelligence

Semi-supervised learning based on nearest neighbor rule and cut edges

Yu Wang et al.

KNOWLEDGE-BASED SYSTEMS (2010)

Article Computer Science, Artificial Intelligence

A classification algorithm based on local cluster centers with a few labeled training examples

Tianqiang Huang et al.

KNOWLEDGE-BASED SYSTEMS (2010)

Article Computer Science, Artificial Intelligence

Genetic algorithm-based training for semi-supervised SVM

Mathias M. Adankon et al.

NEURAL COMPUTING & APPLICATIONS (2010)

Article Computer Science, Artificial Intelligence

Self-supervised ARTMAP

Gregory P. Amis et al.

NEURAL NETWORKS (2010)

Article Computer Science, Artificial Intelligence

Semi-supervised learning for tree-structured ensembles of RBF networks with Co-Training

Mohamed Farouk Abdel Hady et al.

NEURAL NETWORKS (2010)

Article Computer Science, Artificial Intelligence

Co-training with relevant random subspaces

Yusuf Yaslan et al.

NEUROCOMPUTING (2010)

Article Computer Science, Artificial Intelligence

Multiple-view multiple-learner active learning

Qingjiu Zhang et al.

PATTERN RECOGNITION (2010)

Article Computer Science, Artificial Intelligence

Semi-supervised clustering with metric learning: An adaptive kernel method

Xuesong Yin et al.

PATTERN RECOGNITION (2010)

Article Computer Science, Artificial Intelligence

SemiBoost: Boosting for Semi-Supervised Learning

Pavan Kumar Mallapragada et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2009)

Article Computer Science, Artificial Intelligence

Discretization for naive-Bayes learning: managing discretization bias and variance

Ying Yang et al.

MACHINE LEARNING (2009)

Article Computer Science, Artificial Intelligence

KEEL: a software tool to assess evolutionary algorithms for data mining problems

J. Alcala-Fdez et al.

SOFT COMPUTING (2009)

Article Computer Science, Artificial Intelligence

Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle

Akinori Fujino et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2008)

Article Computer Science, Artificial Intelligence

Learning to detect moving shadows in dynamic environments

Ajay J. Joshi et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2008)

Article Computer Science, Artificial Intelligence

Locality sensitive semi-supervised feature selection

Jidong Zhao et al.

NEUROCOMPUTING (2008)

Article Computer Science, Artificial Intelligence

A unified framework for semi-supervised dimensionality reduction

Yangqiu Song et al.

PATTERN RECOGNITION (2008)

Article Computer Science, Artificial Intelligence

Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

Yuanqing Li et al.

MACHINE LEARNING (2008)

Article Computer Science, Cybernetics

Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples

Ming Li et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS (2007)

Article Automation & Control Systems

A lot of randomness is hiding in accuracy

Arle Ben-David

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2007)

Article Computer Science, Artificial Intelligence

Tri-training: Exploiting unlabeled data using three classifiers

ZH Zhou et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2005)

Article Computer Science, Artificial Intelligence

Text classification from labeled and unlabeled documents using EM

K Nigam et al.

MACHINE LEARNING (2000)