Feature (machine learning)In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
Entropy (statistical thermodynamics)The concept entropy was first developed by German physicist Rudolf Clausius in the mid-nineteenth century as a thermodynamic property that predicts that certain spontaneous processes are irreversible or impossible. In statistical mechanics, entropy is formulated as a statistical property using probability theory. The statistical entropy perspective was introduced in 1870 by Austrian physicist Ludwig Boltzmann, who established a new field of physics that provided the descriptive linkage between the macroscopic observation of nature and the microscopic view based on the rigorous treatment of large ensembles of microstates that constitute thermodynamic systems.
Branch predictorIn computer architecture, a branch predictor is a digital circuit that tries to guess which way a branch (e.g., an if–then–else structure) will go before this is known definitively. The purpose of the branch predictor is to improve the flow in the instruction pipeline. Branch predictors play a critical role in achieving high performance in many modern pipelined microprocessor architectures. Two-way branching is usually implemented with a conditional jump instruction.
Model selectionModel selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection.
Document classificationDocument classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science.
Speculative executionSpeculative execution is an optimization technique where a computer system performs some task that may not be needed. Work is done before it is known whether it is actually needed, so as to prevent a delay that would have to be incurred by doing the work after it is known that it is needed. If it turns out the work was not needed after all, most changes made by the work are reverted and the results are ignored. The objective is to provide more concurrency if extra resources are available.
American SamoaAmerican Samoa (Amerika Sāmoa, aˈmɛɾika ˈsaːmʊa; also Amelika Sāmoa or Sāmoa Amelika) is an unincorporated territory of the United States located in the South Pacific Ocean, southeast of the island country of Samoa. Centered on , it is east of the International Date Line and the Wallis and Futuna Islands, west of the Cook Islands, north of Tonga, and some south of Tokelau. American Samoa is the southernmost territory of the United States and one of two U.S. territories south of the Equator, along with the uninhabited Jarvis Island.
Consensus decision-makingConsensus decision-making or consensus process (often abbreviated to consensus) are group decision-making processes in which participants develop and decide on proposals with the aim, or requirement, of acceptance by all. The focus on establishing agreement of at least the majority or the supermajority and avoiding unproductive opinion differentiates consensus from unanimity, which requires all participants to support a decision. The word consensus is Latin meaning "agreement, accord", derived from consentire meaning "feel together".
Gradient boostingGradient boosting is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest.
American cuisineAmerican cuisine consists of the cooking style and traditional dishes prepared in the United States. It has been significantly influenced by Europeans, indigenous Native Americans, Africans, Asians, Pacific Islanders, and many other cultures and traditions. Principal influences on American cuisine are Native American, soul food, regional heritages including Cajun, Louisiana Creole, Mormon foodways, New Mexican, Pennsylvania Dutch, Texan, Tex-Mex, and Tlingit, and the cuisines of immigrant groups such as American Chinese, Italian American, Greek American and Mexican American.
Tyranny of the majorityThe tyranny of the majority (or tyranny of the masses) is an inherent weakness to majority rule in which the majority of an electorate pursues exclusively its own objectives at the expense of those of the minority factions. This results in oppression of minority groups comparable to that of a tyrant or despot, argued John Stuart Mill in his 1859 book On Liberty. The scenarios in which tyranny perception occurs are very specific, involving a sort of distortion of democracy preconditions: Centralization excess: when the centralized power of a federation make a decision that should be local, breaking with the commitment to the subsidiarity principle.
Predictive analyticsPredictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.
