Clustering and pattern recognition W ikipedia entry on machine learning 71 Decision tree learning 72 Association rule learning 73 Artificial neural networks 74 Genetic programming 75 Inductive logic programming ID: 387982
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Rerun of machine learning
Clustering and pattern recognitionSlide2
Wikipedia entry on machine learning
7.1 Decision tree learning
7.2 Association rule learning
7.3 Artificial neural networks
7.4 Genetic programming
7.5 Inductive logic programming
7.6 Support vector machines
7.7 Clustering
7.8 Bayesian networks
7.9 Reinforcement learning
7.10 Representation learning
7.11 Sparse Dictionary Learning
And many are still missing (ant colonies; game theory; Laplace approximations; maximum entropySlide3
Supervised versus unsupervised
Some methods really learn by themselves, but others are based on a training and testing set.Slide4
Clustering or rules
Most times machine learning is used to cluster groups of data.
It is also possible to find trends or rules with no clusters involved.Slide5
Force fields
There is a whole class of machine learning algorithms that use force fields (especially if clustering is involved).
These will be discussed separately.Slide6
Difficulty
It normally is not easy to determine which method to use given the problem at hand. Some rules of thumb:
1) If you feel that a force field could do the job, but you also feel that there is a non-linear relation between data and response, then use an artificial neural network.
2) When dealing with optimisation in a high dimensional space, look into genetic algorithms.Slide7
3) When clustering things without clearly detectable clusters, look into
SVMs
.
4) When you have much more data than is needed, look into random forest methods.
5) When it seems clear how to deal with the data, but there are too many choices, look into decision tree methods.
6) When dealing with sequence
(-like