Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
Sequential analysisIn statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data are evaluated as they are collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost.
Ambient intelligenceIn computing, ambient intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. Ambient intelligence is a projection of the future of consumer electronics, telecommunications, and computing, originally developed in the late 1990s by Eli Zelkha and his team at Palo Alto Ventures for the time frame 2010–2020. This concept is intended to enable devices to work in concert with people in carrying out their everyday life activities, tasks, and rituals in an intuitive way by using information and intelligence hidden in the network connecting these devices (for example: The Internet of Things).
Adaptive market hypothesisThe adaptive market hypothesis, as proposed by Andrew Lo, is an attempt to reconcile economic theories based on the efficient market hypothesis (which implies that markets are efficient) with behavioral economics, by applying the principles of evolution to financial interactions: competition, adaptation, and natural selection. This view is part of a larger school of thought known as Evolutionary Economics. Under this approach, the traditional models of modern financial economics can coexist with behavioral models.
Working hypothesisA working hypothesis is a hypothesis that is provisionally accepted as a basis for further ongoing research in the hope that a tenable theory will be produced, even if the hypothesis ultimately fails. Like all hypotheses, a working hypothesis is constructed as a statement of expectations, which can be linked to deductive, exploratory research in empirical investigation and is often used as a conceptual framework in qualitative research. The term "working" indicates that the hypothesis is subject to change.
