C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Lazy learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning from examples: inductive and lazy methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Information complexity of neural networks
Neural Networks
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Lazy Learning of Bayesian Rules
Machine Learning
Machine Learning
Robust Classification for Imprecise Environments
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Feature Generation Using General Constructor Functions
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
Automatic Video Database Indexing and Retrieval
Multimedia Tools and Applications
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Combining Classifiers with Meta Decision Trees
Machine Learning
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Artificial Neural Network Learning: A Comparative Review
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Effects of domain characteristics on instance-based learning algorithms
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
A visual search system for video and image databases
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
The Knowledge Engineering Review
Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A classification paradigm for distributed vertically partitioned data
Neural Computation
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Agent-based distributed data mining: the KDEC scheme
Intelligent information agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Naive bayes classifiers that perform well with continuous variables
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Explaining the output of ensembles in medical decision support on a case by case basis
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Music review classification enhanced by semantic information
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Enhanced Topic-based Vector Space Model for semantics-aware spam filtering
Expert Systems with Applications: An International Journal
Classification of user postures with capacitive proximity sensors in AAL-Environments
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Cross-document structural relationship identification using supervised machine learning
Applied Soft Computing
Automatic categorisation of comments in social news websites
Expert Systems with Applications: An International Journal
Computer-aided techniques for chromogenic immunohistochemistry: Status and directions
Computers in Biology and Medicine
Opcode sequences as representation of executables for data-mining-based unknown malware detection
Information Sciences: an International Journal
T-Recs: Time-aware Twitter-based Drug Recommender System
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Enriching media fragments with named entities for video classification
Proceedings of the 22nd international conference on World Wide Web companion
Peer-to-peer data mining classifiers for decentralized detection of network attacks
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
Risk analysis of electronic transactions in tourism web applications
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Multi model transfer learning with RULES family
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Reviewing traffic classification
DataTraffic Monitoring and Analysis
Retrieval of high-dimensional visual data: current state, trends and challenges ahead
Multimedia Tools and Applications
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The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single chapter cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.