8 skills found
LearnLib / LearnlibA free, open-source Java library for automata learning algorithms
LearnLib / AlexAutomata Learning EXperience (ALEX) - A free and open-source web application for testing and learning web applications via active automata learning
Simon-ux / Free Algorithm Learning算法导航，免费的可视化算法学习神器！通过交互式动画带你掌握常见数据结构（二叉树、链表、栈、队列、图）和算法（排序、搜索、动态规划、贪心）。提供保姆级算法学习路线图和详细教程，支持Java、Python、JavaScript、Go、C++等多语言实现。可自定义输入数据，实时观察算法执行过程，配套LeetCode练习题。告别抽象难懂的传统学习方式，完美应对算法面试。从入门到高阶，让算法学习变得简单、直观、有趣！
mvcisback / LstarPython implementation of lstar automata learning algorithm.
LearnLib / RalibLibrary for active learning algorithms for register automata
hmofrad / Dynamic PSO LAImproving Learning Automata based Particle Swarm: An Optimization Algorithm
rezashokrzad / Natural Computing NCEvolutionary Algorithms (EA), Canonical Genetic Algorithm (CGA), Swarm Intelligence (SI), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Immune Systems for Anomaly Detection, Cellular Automata, Ensemble Learning
TxusLopez / CURIEData stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CU RIE, a drift detector relying on cellular automata. Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CU RIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CU RIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.
