Learning Algorithms

The below lists various Algorithms and Learning Techniques used in Artificial Intelligence and Machine Learning applications and systems.

Learning Algorithms

Below we list resources that give details on the different forms of learning algorithms and techniques available.

Please note this is by no means an extensive list of algorithms, give the expansive nature of the field it would be very difficult to capture every new algorithm that is being developed by the research community, however, we are trying to list the algorithms and technicals that are considered mainstream. If you would like us to add a specific learning algorithm resource to this list, please let us know via our add a link page


Overviews of Learning Algorithms


Neural Networks

  • A good source to learn Recurrent Neural Nets and Long Short Term Memory Nets via Reddit

    Fuzzy Logic

    • Fuzzy Mail List – archive by thread
    • EBRSC– Electronic Bulletin of the Rough Set Community
    • Fuzzy Systems by James F. Brule – Covers the history, main concepts, applications and peer objections. Includes bibliography and additional resources.
    • Fuzzy Logic – Survey of logical systems with a continuum of truth values; from the Stanford Encyclopedia by Petr Hajek.
    • Fuzzy Logic Sources – Maintained by Bob John, De Montfort University.
    • Fuzzy Logic Archive – The Net’s Original Fuzzy Logic Archive – Since 1994.
    • Fuzzy Logic – Open Encyclopedia article.
    • Many-Valued Logic – Survey article on multiple-valued logics, by Siegfried Gottwald. (from the Stanford Encyclopedia)
    • The Logic of More – Analysis of a ripple adder in binary logic and alternative designs in ternary and multi-value logics.
    • Polyvalued Logic – A general math defined first order and modal propositional logic based on degrees of truth other than fuzzy concepts.
    • Multi-valued Logic Home Page – Links to journals and researchers in many-valued logic, bibliographies, and conferences.


    Case Based Reasoning

    • myCBR MyCBR-Project.net


    Genetic Algorithms and Evolutionary Computing


    Belief Networks (Bayesian)

    • Association for Uncertainty in Artificial Intelligence – Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
    • Qualitative Verbal Explanations in Bayesian Belief Networks – Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
    • Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference – Article published in JAIR (Journal of AI Research) about a way to implement belief networks by compiling networks into arithmetic expressions and then answering queries using an evaluation algorithm.
    • A Brief Introduction to Graphical Models and Bayesian Networks – Kevin Murphy’s tutorial, including a recommended reading list.
    • Cause, chance and Bayesian statistics – Briefing document with a short survey of Bayesian statistics
    • Learning Bayesian Networks from Data – Slides and additional notes from a tutorial by Nir Friedman and Daphne Koller on automated learning of belief networks, given at the Neural Information Processing Systems (NIPS-2001) conference
    • Daphne’s Approximate Group of Students (DAGS) – Daphne Koller’s research group on probabilistic representation, reasoning, and learning at Stanford University
    • Belief Networks and Variational Methods : Amos Storkey – Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
    • Kevin Murphy’s list of Bayesian network software
    • Bayesian Network Toolbox (BNT) – Kevin Murphy’s MATLAB toolbox – supports dynamic BNs, decision networks, many exact and approximate inference algorithms, parameter estimation, and structure learning
    • HUGIN EXPERT Developers of the Hugin tool
    • Bayesian inference Using Gibbs Sampling (BUGS) – Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods.
    • JavaBayes – Bayesian networks in Java.
    • Belief Net Power Constructor – System based on Jie Cheng’s three-phase belief network construction algorithm. Includes a wizard-like user interface and a belief network construction engine.
    • RISO – Robert Dodier’s open source package for distributed, heterogeneous belief networks in Java – allows different conditional distributions
    • BayesiaLab – Bayesian network laboratory producing a broad set of tools for structure learning, analysis, adaptive questionnaires, and dynamic Bayesian networks
    • Bayesline – Very generic and free (LGPL) Belief Network Framework in C++ – supports a broad range of knowledge and dependency types for network variables and clusters of variables
    • Bayesian Network tools in Java (BNJ) – Open-source suite of software tools for research and development using graphical models of probability, published by Kansas State University Laboratory for Knowledge Discovery in Databases (KDD)
    • BayesBuilder – A tool for constructing and testing Bayesian networks.
    • BNet Desktop Software and Developer Toolkits – BNet.Builder is a belief network software application. BNet.EngineKit provides an embeddable engine.
    • SamIam: Sensitivity Analysis, Modeling, Inference and More – A tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA.
    • WebWEavr-III – a Java application that supports the construction of Bayesian networks, inference in standard and dynamic Bayesian networks and decomposable Markov networks, construction and verification of multiply-sectioned Bayesian networks (MSBNs), inference in multi-agent MSBNs, and learning decomposable Markov networks.
    • IBAyes – a probabilistic reasoning tool that allows its user to model uncertain situations and to perform inference using Bayesian networks and its variants such as Influence Nets. The tool is developed by the Artificial Intelligence Lab @ IBA and currently works in Windows environment.
    • Lumina Decision Systems – Makers of Analytica, visual software tool for creating, analyzing, and communicating quantitative business models.
    • Knowledge Industries, Inc. – Builds and licenses diagnostic software based upon Bayesian Belief Networks for medical, industrial and management applications. Software includes editors/compilers, test/review tools and inference engines embeddable in stand-alone and web-based applications.
    • Norsys Software Corp. – Netica is a complete program for working with belief networks and influence diagrams. Feature compiles belief (Bayesian) networks into a junction tree of cliques for fast probabilistic reasoning.
    • DecsionQ Bayesian Predictive Analysis Software – A data mining software company that has a fully automated data modeling and predictive analytics package.
    • AgenaRisk – Bayesian network, simulation and risk analysis software. Supports exact and approximate inference in hybrid and dynamic networks for decision support, diagnosis, statistical learning and prediction applications.
    • Complex Systems Computation Group (CoSCo) – BAYDA software implements Bayesian predictive discriminant analysis, where the aim is to build a model for predicting the value of one discrete (class, group, category) variable using other variables.



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