Machine Learning Overview
Taiwo Oladipupo · InTech eBooks · 2010
OverviewMachine Learning according to Michie et al (D. Michie, 1994) is generally taken to encompass automatic computing procedures based on logical or binary operations that learn a task from a series of examples.Here we are just concerned with classification, and it is arguable what should come under the Machine Learning umbrella.Attention has focussed on decision-tree approaches, in which classification results from a sequence of logical steps.These are capable of representing the most complex problem given sufficient data (but this may mean an enormous amount!).Other techniques, such as genetic algorithms and inductive logic procedures (ILP), are currently under active development and in principle would allow us to deal with more general types of data, including cases where the number and type of attributes may vary, and where additional layers of learning are superimposed, with hierarchical structure of attributes and classes and so on.Machine Learning aims to generate classifying expressions simple enough to be understood easily by the human.They must mimic human reasoning sufficiently to provide insight into the decision process.Like statistical approaches, background knowledge may be exploited in development, but operation is assumed without human intervention.To learn is: to gain knowledge, comprehension, or mastery of through experience or study or to gain knowledge (of something) or acquire skill in (some art or practice) to acquire experience of or an ability or a skill in to memorize (something), to gain by experience, example, or practice.Machine Learning can be defines as a process of building computer systems that automatically improve with experience, and implement a learning process.Machine Learning can still be defined as learning the theory automatically from the data, through a process of inference, model fitting, or learning from examples: Automated extraction of useful information from a body of data by building good probabilistic models. Ideally suited for areas with lots of data in the absence of a general theory.