Machine Evolutionary Behavior by Embedded Collaborative Learning Engine (eCLE)

A system of Embedded Collaborative Learning Engine (eCLE) comprising a supervised learning block, a unsupervised learning block and an LVQ network, comprising an initialization process which includes the following steps:

• (a) defining a supervised classifier 22 to perform classification for a subset formed by known characterized patterns after pattern/class characterization, wherein the universe is considered as the set of all possible patterns in a system including characterized and non characterized ones, wherein an initial value of M2 is defined by the number of elements in the subset, wherein the supervised classifier 22 is designed by an online learning technique, where M2 is the number of characterized classes;
• (b) then performing unsupervised clustering by an unsupervised clustering algorithm which is embedded and applied to a training data file for the subset, thereby unsupervised and supervised classifiers are obtained;
• (c) obtaining eCLE kernel in a dynamic and autonomous way through the LVQ network 60,
• wherein the LVQ network is arranged for fusing knowledge and providing a framework for finding interrelations among classes, subclasses and available knowledge such that interrelations within unsupervised clusters and mapping to known classes from supervised learning can be identified for initializing the kernel, wherein new emerging relations is identified and added through a framework provided by the kernel by unsupervised clustering in unsupervised learning block in a systematic way, thereby the system is capable of providing evolving behavior within the system.

A method of Embedded Collaborative Learning Engine (eCLE) which comprises a supervised learning block, an unsupervised learning block and an LVQ network, comprising the steps of:

• (a) transferring available knowledge within a system through supervised learning, therefore available knowledge about classes and patterns is obtained;
• (b) recognizing new clusters through unsupervised learning which allows cluster identification and characterization;
• and
• (c) enabling autonomous learning and adaptation through embedding fast on-line learning algorithms such that a kernel with a plurality of preset learning schemes to which includes unsupervised, supervised, and hybrid learning schemes can be triggered and executed within a generalized framework, thereby an efficient scheme for enabling autonomous system evolution can be selectively provided.