Search Results for "learning classifier system" - Page 18

Self Organization of an adaptive learning framework for pervasive graph structures from grids, utilities to semantic governance. Business, performance and provisioning assurance are converged with EGA, ITIL, eTOM,SOX, HIPPA, CobiT and AC

pyPal is a jabber based chatterbot that can be used to launch commands remotely as well as to make some good conversation. It is expected to be capable of multi-language learning.

AISK is an acronym for Artificial Intelligence Spam Killer. It is a server side filter that acts as a kind of PROXY and uses SMTP to communicate with MTA. Main decision mechanism is based on artificial neural network capable of finding patterns, learning

WHEREAMI is an intelligent and self-learning network detection and configuration utility. With this capability, a user can just walk to a network range and WHEREAMI will self determine and connect to that network with relevant policies.

Hippal is an integrating GUI-framework for a multimodular symbolic A.I.-system IPAL, which combines A.I. Planning, Inductive Program Synthesis, Analogical Reasoning and Learning. Hippal is client-/server-based and uses the Lili Lisp Interpreter as Centr

A fast decision tree program featuring distributed ensemble learning across multiple processors using MPI. Has been run on 1 cpu, small clusters, and the ASCI Blue supercomputer. Ensembles are often more accurate than their single classifier counterparts

Artificial vision library. Objectives are to make an OCR, fingerprint and face identification as some applications through a general purpose learning and pattern relationships algorithm (Currently performs very basic identification).

Java library devoted to handle Genetic Algorithms and Classifier Systems. It has been engineered to be used into agent based simulation models and to search bounded optimal solutions in wide solution spaces. It runs on distributed clusters.

A race car simulator which allows learning computer agents to compete against each other.

CompLearn is a general-purpose compression-based machine learning system that uses data compression to learn or data mine patterns in arbitrary data.

A collection of open source software and documents on machine perception and machine learning. Includes a state of the art face detector (MPISearch), video labeling tools (Score), and tutorials (Kolmogorov Tutorials).

FLPD is an automatic learning system based on fuzzy prototypes, composed of a C++ library for machine learning and fuzzy logic and an experimentation framework.

The TreeQ package is a set of C-language applications that implement a automatic machine learning algorithm based on a tree-structured classifier. This approach is particularly effective for high-dimensional continuous data such as audio and video.

The purpose of the project is to make the bash behave intelligent. This includes currently corrrection of mistyped program names, autocorrection while pressing TAB and learning which program are associated with which suffix.

Net.py is a tool for learning about neural nets. Currently, it only allows the user to experiment with a Hopfield net. I am now extending it to cover the Kohonen net. I'd be pleased to receive suggestions and criticism.

GNNS - GNNS Neural Network Simulator, is both a set of libraries and an interface for creating and learning neural networks. It is aimed to support as many network typs and learning algorithms as possible. GNNS is meant to support different platforms.

HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.

Neural Network calculation & learning library

iMate is a win32 / Linux desktop mate programme. It offers more features than the already existing desktop mate (AI, learning capacity, growth and limited lifeTime, third party tools to add new behaviour, ...).

pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due...

Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection -...

a distributed engine for abstract neural network development via natural-language programming

...With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30% improvement in programming scaffolds. M2.7 excels in real-world engineering scenarios, including debugging, log analysis, system monitoring, and root cause investigation, demonstrating strong system-level reasoning comparable to SRE workflows. ...

SuperJabberBot is a project that aims to implements an extendible and very flexible bot for the jabber protocoll in c++.Chatting with the bot you can control a remote server without ssh, and can also be used as a chat bot, learning language from the chat

...The model supports a 1 million token context window, enabling it to maintain coherence across long workflows involving thousands of tool calls and multi-step reasoning chains. Architecturally, it uses a hybrid attention system combining Sliding Window Attention and Global Attention to significantly reduce memory usage while preserving long-context performance. It also integrates multi-token prediction modules that accelerate inference and improve reinforcement learning efficiency. Trained on around 27 trillion tokens with FP8 mixed precision and refined through supervised fine-tuning, large-scale agentic reinforcement learning, and distillation.