Combinatorics is defined as a branch of mathematics that is concerned with sets of objects that meet certain conditions. In computer science, combinatorics is used to study algorithms, sets of steps or rules devised to address a specific problem. Combinators optimisation is a subfield of combinators related to algorithm theory machine learning, image analysis and ANNs. Machine learning is related to computational statistics, which focuses on prediction making through the use of computers. Combinators are nothing but the study of countable sets. Probability use combinators to assign probability value between 0 to 1 to events and compare them with probability models. Real-world machine learning tasks frequently involve combinatorial structure.

How model, infer or predict with graphs, matchings, hierarchies, informative subsets or other discrete structures are underlying the data In Artificial neural networks, feature selection and parameter optimisation in feed-forward artificial neural networks. In feature selection, you’re trying to find an optimal combination of features to use in your dataset from a finite possible selection. Greedy algorithms, meta-heuristics and information gain filtering are all common approaches. Back-propagation is an algorithm used in artificial neural networks to find a near-optimal set of weights/parameters.