Algorithm types
Machine learning algorithms can be organized into a taxonomy based on the desired outcome of the algorithm.
- Supervised learning generates a function that maps inputs to desired outputs (also called labels, because they are often provided by human experts labeling the training examples). For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function.
- Unsupervised learning models a set of inputs, like clustering. See also data mining and knowledge discovery.
- Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier.
- Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm.
- Transduction tries to predict new outputs based on training inputs, training outputs, and test inputs.
- Learning to learn learns its own inductive bias based on previous experience.
Approaches
Main article: List of machine learning algorithms
Decision tree learning
Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.
[edit]Association rule learning
Association rule learning is a method for discovering interesting relations between variables in large databases.
[edit]Artificial neural networks
An artificial neural network (ANN) learning algorithm, usually called "neural network" (NN), is a learning algorithm that is inspired by the structure and/or functional aspects of biological neural networks. Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.
[edit]Genetic programming
Genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
[edit]Inductive logic programming
Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples.
[edit]Support vector machines
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
[edit]Clustering
Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis.
[edit]Bayesian networks
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and theirconditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inferenceand learning.
[edit]Reinforcement learning
Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.
[edit]Representation learning
Several learning algorithms, mostly unsupervised learning algorithms, aim at discovering better representations of the inputs provided during training. Classical examples include principal components analysis and clustering. Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing to reconstruct the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse (has many zeros). Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.[3]
[edit]Sparse Dictionary Learning
In the learning area, sparse dictionary learning is one of the most popular methods, and has gained a huge success in lots of applications. In sparse dictionary learning, a data is represented as a linear combination
of basis functions, and the coefficients are assumed to be sparse. Let x be a d-dimensional data, D be a d byn matrix, where each column of D represent a basis function. r is
the coefficient to represent x using D. Mathematically, sparse dictionary learning means the following 
where r is sparse. Generally speaking, n is assumed to be larger than d to allow the freedom for a sparse representation.
Sparse dictionary learning has been applied in different context. In classification, the problem is to determine whether a new data belongs to which classes. Suppose we already build a dictionary for each class, then a new data is associate to the class such that it is best sparsely represented by the corresponding dictionary. People also applied sparse dictionary learning in image denoising. The key idea is that clean image path can be sparsely represented by a image dictionary, but the noise cannot. User can refer to [4] if interested.
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Algorithms
The most widely used classifiers are the neural network (multi-layer perceptron), support vector machines, k-nearest neighbours, Gaussian mixture model, Gaussian,naive Bayes, decision tree and RBF classifiers.
Examples of classification algorithms include: