MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.
QSMM, the "QSMM State Machine Model", is a framework for development of non-deterministic intelligent state models and systems with spur-driven behavior. It includes low-level functions for generating optimal actions by the system and high-level functions for building multinode models. In a multinode model, nodes represent components of a system you develop which choose optimal actions using the framework and can correspond to entities external to the system and which behavior is to be learnt. A node can choose optimal actions based on a current node state which is either set manually by your program or is identified automatically by the framework. Probability profiles for a state transition matrix and an action emission matrix of the node can be specified using an assembler program with a user-defined instruction set.
Into is a cross-platform machine intelligence application framework written in C++. Into provides a different, fast way to build high-performance applications for image analysis, machine vision, pattern recognition, and artificial intelligence. It features a layered API and more than 20 fully interoperable plug-in modules for accessing image and data sources, powerful feature extractors, classifiers, neural networks, and much more. It also provides Ydin, an innovative execution engine that makes it easy to create dynamic programs that automatically run in parallel, enabling you to create more with less hassle, less code, and less time. Into uses Qt to let you create beautiful user interfaces for your applications with ease.
Bayesian Spam Filter is a class that can be used to detect spam in text messages using Bayesian techniques. It analyzes the text in terms of n-grams in a way that is idiom independent. It can be trained to progressively distinguish what is spam and what is not spam by detecting patterns in training samples. Training data is stored in a MySQL database.
Amiba is a Gene Expression Programming (GEP) framework for Java. GEP is, like genetic algorithms, a branch of evolutionary computing. The framework separates the process of evolution from the process of interpretation of the chromosome, allowing the use of various schemes. For example, graphs may be used as terminals and graph operations as operators in the chromosome instead of the usual double precision numbers. It implements mutation, transposition, and recombination. Options and rates are easily configured through an XML file. A mechanism to load fitness cases in bulk is also provided.
The XEVM is an XML processing engine. It's a multi-threaded, Pub/Sub environment for dynamic programming on an event-driven state machine with TCP communications, tight fault free memory management, powerful set algebra, and a magical database. It is 100% C++ (25,000 LOC), with a thin porting layer; there are implementations for POSIX (Mac/Linux) and Win32. The XEVM is for processing XEPL (the Xepl Engine Programming Language).
The main goal of the Cognitive Vision project is to improve the results of artificial intelligence. The current scope is to equip the abilities of the AIBO (robot dog of the Sony) to explore its environment and to recognize the local position (e.g. navigation in a room). The first trial is to use and develop image processing methods with a single webcam and to apply these techniques with AIBO.