Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
Altaic languagesAltaic (ælˈteɪ.ᵻk) is a controversial proposed language family that would include the Turkic, Mongolic and Tungusic language families and possibly also the Japonic and Koreanic languages. The hypothetical language family has long been rejected by most comparative linguists, although it continues to be supported by a small but stable scholarly minority. Speakers of the constituent languages are currently scattered over most of Asia north of 35° N and in some eastern parts of Europe, extending in longitude from Turkey to Japan.
Minority languageA minority language is a language spoken by a minority of the population of a territory. Such people are termed linguistic minorities or language minorities. With a total number of 196 sovereign states recognized internationally (as of 2019) and an estimated number of roughly 5,000 to 7,000 languages spoken worldwide, the vast majority of languages are minority languages in every country in which they are spoken. Some minority languages are simultaneously also official languages, such as Irish in Ireland or the numerous indigenous languages of Bolivia.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Feature selectionFeature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times, to avoid the curse of dimensionality, improve data's compatibility with a learning model class, encode inherent symmetries present in the input space.
Feature (computer vision)In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.
Indigenous languages of the AmericasOver a thousand indigenous languages are spoken by the Indigenous peoples of the Americas. These languages cannot all be demonstrated to be related to each other and are classified into a hundred or so language families (including a large number of language isolates), as well as a number of extinct languages that are unclassified because of a lack of data. Many proposals have been made to relate some or all of these languages to each other, with varying degrees of success.
Morphology (linguistics)In linguistics, morphology (mɔrˈfɒlədʒi ) is the study of words, how they are formed, and their relationship to other words in the same language. It analyzes the structure of words and parts of words such as stems, root words, prefixes, and suffixes. Morphology also looks at parts of speech, intonation and stress, and the ways context can change a word's pronunciation and meaning. Morphology differs from morphological typology, which is the classification of languages based on their use of words, and lexicology, which is the study of words and how they make up a language's vocabulary.
Named-entity recognitionNamed-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. Most research on NER/NEE systems has been structured as taking an unannotated block of text, such as this one: Jim bought 300 shares of Acme Corp.
Facial recognition systemA facial recognition system is a technology potentially capable of matching a human face from a or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics.
Discrimination based on skin colorDiscrimination based on skin color, also known as colourism, or shadeism, is a form of prejudice and discrimination in which people of certain ethnic groups or people who are perceived as belonging to a darker skinned race are treated differently based on their darker skin color. Although less historically significant, prejudice can also be applied towards lighter-skinned people, which is known as reverse colourism.
Statistical machine translationStatistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory.
