Hidden Markov Models, Machine Learning, Support Vector Machines

Hidden Markov Models (HMMs) provide a formal foundation for creating probabilistic models of linear sequence 'labelling' problems. They offer a conceptual framework for creating sophisticated models just by drawing an image. Gene finding, profile searches, multiple sequence alignment, and regulatory site identification are just a few of the tools that use them. The Legos of computational sequence analysis are HMMs.

Machine learning (ML) is the study of computer algorithms that can learn and develop on their own with experience and data. It is considered to be a component of artificial intelligence. Machine learning algorithms create a model based on training data to make predictions or judgments without having to be explicitly programmed to do so. Machine learning algorithms are utilized in a wide range of applications, including medicine, email filtering, speech recognition, and computer vision, where developing traditional algorithms to do the required tasks is difficult or impossible. However, not all machine learning is statistical learning. A subset of machine learning is strongly related to computational statistics, which focuses on making predictions using computers.

Support-vector machines (SVMs, also known as support-vector networks) are supervised learning models that examine data for classification and regression analysis in machine learning. Many people prefer the support vector machine because it produces great accuracy while using less computing power. SVM (Support Vector Machine) is a type of machine that may be used for both regression and classification. However, it is extensively employed in categorization goals.