machine learning
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This website is a wiki for research in On-line Prediction . It provides two resources: There are both articles about theory and articles about experimental results (we try to separate theory and experiments, covering them in different, often cross-referenced, articles).
on-line prediction wiki - Wiki for On-Line Prediction
Graphical model - Wikipedia, the free encyclopedia
A graphical model is a probabilistic model for which a graph denotes the conditional independence structure between random variables .
Inference - Wikipedia, the free encyclopedia
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. [ 1 ] The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic . Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology ; artificial intelligence researchers develop automated inference systems to emulate human inference.
Convex polytope - Wikipedia, the free encyclopedia
A 3-dimensional convex polytope A convex polytope is a special case of a polytope , having the additional property that it is also a convex set of points in the n -dimensional space R n . [ 1 ] Some authors use the terms "convex polytope" and "convex polyhedron" interchangeably, while others prefer to draw a distinction between the notions of a polyhedron and a polytope. In addition, some texts require a polytope to be a bounded set , while others [ 2 ] (including this article) allow polytopes to be unbounded.
UAI - Uncertainty in Artificial Intelligence
Tree-reweighted max-product (TRW) message passing is a modified form of the ordinary max-product algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a configuration that is provably optimal. In this paper, we focus on the case of binary variables with pairwise couplings, and establish stronger properties of TRW fixed points that satisfy only the milder condition of weak tree agreement (WTA).Markov chain Monte Carlo ( MCMC ) methods (which include random walk Monte Carlo methods) are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution .
Markov chain Monte Carlo - Wikipedia, the free encyclopedia
Belief propagation , also known as Sum-product message passing is a message passing algorithm for performing inference on graphical models , such as Bayesian networks and Markov random fields .
Belief propagation - Wikipedia, the free encyclopedia
This is the first of two papers presenting and evaluating the power of a new framework for combinatorial optimization in graphical models, based on AND/OR search spaces.
Artificial Intelligence : AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
In a Bayesian network, the Markov blanket of node A includes its parents, children and the other parents of all of its children. In machine learning , the Markov blanket for a node in a Bayesian network is the set of nodes composed of 's parents, its children, and its children's other parents.
Markov blanket - Wikipedia, the free encyclopedia
A Bayesian network , Bayes network , belief network , hierarchical Bayes(ian) model or directed acyclic graphical model is a probabilistic graphical model (a type of statistical model ) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
Bayesian network - Wikipedia, the free encyclopedia
An example of a Markov random field.
Markov random field - Wikipedia, the free encyclopedia
Multi-armed bandit - Wikipedia, the free encyclopedia
A multi-armed bandit is like a slot machine with multiple levers.Contextual Bandits « Machine Learning (Theory)
One of the fundamental underpinnings of the internet is advertising based content. This has become much more effective due to targeted advertising where ads are specifically matched to interests.In probability theory and statistics , a Gaussian process is a stochastic process whose realisations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution . Moreover, every finite collection of those random variables has a multivariate normal distribution . Gaussian processes are important in statistical modelling because of properties inherited from the normal.
Gaussian process - Wikipedia, the free encyclopedia
Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on the performance of learning algorithms . [ edit ] Background Consider the following situation, which is a general setting of many supervised learning problems.