User:Gk.mansoor/Books/Machine Learning Algorithms - An Overview
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Machine Learning Algorithms
An Overview
- Introduction
- Class membership probabilities
- Computational learning theory
- Data mining
- Inductive bias
- Machine learning
- Overfitting
- Version space
- Supervised Learning - Types
- Active learning (machine learning)
- Learning to rank
- Semi-supervised learning
- Structured prediction
- Supervised learning
- Supervised Learning Algorithms
- Backpropagation
- Boosting (machine learning)
- Case-based reasoning
- Data pre-processing
- Decision tree learning
- Ensemble learning
- Inductive logic programming
- K-nearest neighbors algorithm
- Kriging
- Learning automata
- Level of measurement
- Minimum message length
- Multilinear subspace learning
- Proaftn
- Probably approximately correct learning
- Random forest
- Ripple-down rules
- Similarity learning
- Statistical relational learning
- Support vector machine
- Variable kernel density estimation
- Supervised Learning - Bayesian Statistics
- Bayesian network
- Bayesian statistics
- Naive Bayes classifier
- Unsupervised Learning - Types
- Adaptive resonance theory
- Artificial neural network
- Blind signal separation
- Cluster analysis
- Hidden Markov model
- Self-organizing map
- Unsupervised learning
- Transduction
- Transduction (machine learning)
- Reinforcement Learning - Types
- Dynamic treatment regime
- Error-driven learning
- Fictitious play
- Learning classifier system
- Optimal control
- Q-learning
- Reinforcement learning
- SARSA
- Temporal difference learning
- Inductive Transfer
- Inductive transfer
- Multi-task learning
- Association Rule Mining
- Association rule learning
- Manifold Learning
- Nonlinear dimensionality reduction
- Deep Learning
- Deep learning