Classification model (Модель классификации) — A type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian.
Classification threshold (Порог классификации) — A scalar-value criterion that is applied to a model’s predicted score in order to separate the positive class from the negative class. Used when mapping logistic regression results to binary classification.
Clinical Decision Support (CDS) (Поддержка принятия клинических решений) – A clinical decision support system is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support, that is, assistance with clinical decision- making tasks [[100 - Clinical Decision Support (CDS) [Электронный ресурс] www.quora.com URL: https://www.quora.com/What-are-clinical-decision-support-systems-What-benefits-do-they-provide (дата обращения 28.02.2022)]].
Clipping (Отсечение) – A technique for handling outliers. Specifically, reducing feature values that are greater than a set maximum value down to that maximum value. Also, increasing feature values that are less than a specific minimum value up to that minimum value. For example, suppose that only a few feature values fall outside the range 40—60. In this case, you could do the following: Clip all values over 60 to be exactly 60. Clip all values under 40 to be exactly 40. In addition to bringing input values within a designated range, clipping can also used to force gradient values within a designated range during training.
Closed dictionary (Закрытый словарь) – In speech recognition systems, a dictionary with a limited number of words, to which the recognition system is configured and which cannot be replenished by the user
Cloud (Облако) – The cloud is a general metaphor that is used to refer to the Internet. Initially, the Internet was seen as a distributed network and then with the invention of the World Wide Web as a tangle of interlinked media. As the Internet continued to grow in both size and the range of activities it encompassed, it came to be known as “the cloud.” The use of the word cloud may be an attempt to capture both the size and nebulous nature of the Internet [[101 - Cloud [Электронный ресурс] // dropbox.com URL: https://www.dropbox.com/ru/business/resources/what-is-the-cloud (дата обращения: 09.02.2022)]].
Cloud computing (Облачные вычисления) is an information technology model for providing ubiquitous and convenient access using the Internet to a common set of configurable computing resources (“cloud”), data storage devices, applications and services that can be quickly provided and released from the load with minimal operating costs or with little or no involvement of the provider.
Cloud robotics (Облачная робототехника) – A field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centred on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots have intelligent “brain” in the cloud. The “brain” consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc. [[102 - Сloud robotics [Электронный ресурс] // digitrode.ru URL: http://digitrode.ru/articles/2683-chto-takoe-oblachnaya-robototehnika.html (http://digitrode.ru/articles/2683-chto-takoe-oblachnaya-robototehnika.html) (дата обращения: 09.02.2022)]]
Cloud TPU (Облачный процессор) – A specialized hardware accelerator designed to speed up machine learning workloads on Google Cloud Platform [[103 - Cloud TPU [Электронный ресурс] github.com URL: https://github.com/tensorflow/tpu (https://github.com/tensorflow/tpu) (дата обращения: 25.02.2022)]]
Cluster analysis (Кластерный анализ) – A type of unsupervised learning used for exploratory data analysis to find hidden patterns or groupings in the data; clusters are modeled with a similarity measure defined by metrics such as Euclidean or probability distance.
Clustering (Кластеризация) is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, etc.
Co-adaptation (Коадаптация) – When neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network’s behavior as a whole. When the patterns that cause co-adaption are not present in validation data, then co-adaptation causes overfitting. Dropout regularization reduces co-adaptation because dropout ensures neurons cannot rely solely on specific other neurons.
Cobweb (Метод COBWEB) – An incremental system for hierarchical conceptual clustering. COBWEB was invented by Professor Douglas H. Fisher, currently at Vanderbilt University. COBWEB incrementally organizes observations into a classification tree. Each node in a classification tree represents a class (concept) and is labeled by a probabilistic concept that summarizes the attribute-value distributions of objects classified under the node. This classification tree can be used to predict missing attributes or the class of a new object.
Code (Код) is a one-to-one mapping of a finite ordered set of symbols belonging to some finite alphabet.
Codec (Кодек) “A codec is the means by which sound and video files are compressed for storage and transmission purposes. There are various forms of compression: ‘lossy’ and ‘lossless’, but most codecs perform lossless compression because of the much larger data reduction ratios that occur [with lossy compression]. Most codecs are software, although in some areas codecs are hardware components of image and sound systems. Codecs are necessary for playback, since they uncompress [or decompress] the moving image and sound files and allow them to be rendered.” [[104 - Codec [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)]]
Cognitive architecture (Когнитивная архитектура) – The Institute of Creative Technologies defines cognitive architecture as: “hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments”
Cognitive computing (Когнитивные вычисления) — is used to refer to the systems that simulate the human brain to help with the decision- making. It uses self-learning algorithms that perform tasks such as natural language processing, image analysis, reasoning, and human – computer interaction. Examples of cognitive systems are IBM’s Watson and Google DeepMind [[105 - Cognitive computing [Электронный ресурс] // habr.com URL: https://habr.com/ru/company/ibm/blog/276855/ (https://habr.com/ru/company/ibm/blog/276855/) (дата обращения: 31.01.2022)]]
Cognitive Maps (Когнитивные карты) Cognitive maps are structured representations of decision depicted in graphical format (variations of cognitive maps are cause maps, influence diagrams, or belief nets). Basic cognitive maps include nodes connected by arcs, where the nodes represent constructs (or states) and the arcs represent relationships. Cognitive maps have been used to understand decision situations, to analyze complex cause-effect representations and to support communication. [[106 - Cognitive Maps [Электронный ресурс] www.igi-global.com URL: https://www.igi-global.com/dictionary/qplan/34624 (https://www.igi-global.com/dictionary/qplan/34624) (дата обращения: 07.07.2022)]]
Cognitive science (Когнитивистика, когнитивная наука) – The interdisciplinary scientific study of the mind and its processes. [[107 - Cognitive science Когнитивная наука и интеллектуальные технологии: Реф. сб. АН СССР. – М.: Ин-т науч. информ. по обществ. наукам, 1991. (дата обращения: 04.02.2022)]]
Cohort (Когорта) – A sample in study (conducted to evaluate a machine learning algorithm, for example) where it is followed prospectively or retrospectively and subsequent status evaluations with respect to a disease or outcome are conducted to determine which initial participants’ exposure characteristics (risk factors) are associated with it.
Cold-Start (Холодный запуск) – A potential issue arising from the fact that a system cannot infer anything for users or items for which it has not gathered a sufficient amount of information yet.
Collaborative filtering (Коллаборативная фильтрация) – Making predictions about the interests of one user based on the interests of many other users. Collaborative filtering is often used in recommendation systems.
Combinatorial optimization (Комбинаторная оптимизация) – In Operations Research, applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects [[108 - Combinatorial optimization [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/107404 (дата обращения: 11.02.2022)]].
Committee machine (Комитетная машина) – A type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response. The combined response of the committee machine is supposed to be superior to those of its constituent experts. Compare ensembles of classifiers.
Commoditization (Коммодитизация) is the process of transforming a product from an elite to a generally available (comparatively cheap commodity of mass consumption)
Common Data Element (CDE) (Общий элемент данных) – Common Data Element is a tool to support data management for clinical research [[109 - Common Data Element (CDE) [Электронный ресурс] // URL: https://techdocs.broadcom.com/us/en/ca-mainframe-software/traditional-management/ca-mics-resource-management/14-1/installing/system-modification/ca-mics-facilities/ca-mics-component-generator-mcg/generator-definition-statements/common-data-element-definition-statements.html (https://techdocs.broadcom.com/us/en/ca-mainframe-software/traditional-management/ca-mics-resource-management/14-1/installing/system-modification/ca-mics-facilities/ca-mics-component-generator-mcg/generator-definition-statements/common-data-element-definition-statements.html) (дата обращения 30.04.2022)]].
Commonsense knowledge (Здравый смысл) — In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as “Lemons are sour”, that all humans are expected to know. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy [[110 - Commonsense knowledge [Электронный ресурс] // wikiaro.ru URL: https://wikiaro.ru/wiki/Commonsense_reasoning (дата обращения: 09.02.2022)]].
Commonsense reasoning (Рассуждения на основе здравого смысла) – A branch of artificial intelligence concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day [[111 - Commonsense reasoning [Электронный ресурс] // sciencedirect.com URL: https://www.sciencedirect.com/topics/computer-science/answer-set-programming#:~:text=Answer%20set%20programming%20is%20an,is%20required%20in%20commonsense%20reasoning. (дата обращения: 09.03.2022)]].
Compiler (Компилятор) is a program that translates text written in a programming language into a set of machine codes. AI framework compilers collect the computational data of the frameworks and try to optimize the code of each of them, regardless of the hardware of the accelerator. The compiler contains programs and blocks with which the framework performs several tasks. The computer memory resource allocator, for example, allocates power individually for each accelerator.
Composite AI (Композитный искусственный интеллект) is the combined application of various AI techniques to improve learning efficiency, expand the level of knowledge representation and, ultimately, to more effectively solve a wider range of business problems.
Compression (Компрессия) A method of reducing the size of computer files. There are several compression programs available, such as gzip and WinZip. [[112 - Compression [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)]]
Computation (Вычисление) is any type of arithmetic or non-arithmetic calculation that follows a well-defined model (e.g., an algorithm) [[113 - Computation [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/Computation (https://en.wikipedia.org/wiki/Computation) (дата обращения: 07.07.2022)]].
Computational chemistry (Вычислительная химия) – A branch of chemistry in which mathematical methods are used to calculate molecular properties, model the behavior of molecules, plan synthesis, search databases, and process combinatorial libraries.
Computational complexity theory (Теория сложности вычислений) – Focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm [[114 - Computational complexity theory [Электронный ресурс] // math-cs.spbu.ru URL: https://math-cs.spbu.ru/courses/teoriya-slozhnosti-vychislenij/ (дата обращения: 09.02.2022)]].
Computational creativity (Also artificial creativity, mechanical creativity, creative computing, or creative computation) (Креативные вычисления) – A multidisciplinary endeavour that includes the fields of artificial intelligence, cognitive psychology, philosophy, and the arts. [[115 - Computational creativity [Электронный ресурс] // hoster.bmstu.ru URL: http://hoster.bmstu.ru/~amas/cources/mv/lect__slides.pdf (http://hoster.bmstu.ru/~amas/cources/mv/lect__slides.pdf) (дата обращения: 14.02.2022)]]
Computational cybernetics (Вычислительная кибернетика) — is the integration of cybernetics and computational intelligence techniques.
Computational efficiency of an agent or a trained model (Вычислительная эффективность агента или обученной модели) is the number of computational resources required by the agent to solve a problem at the inference stage.
Computational efficiency of an intelligent system (Вычислительная эффективность интеллектуальной системы) is the amount of computing resources required to train an intelligent system with a certain level of performance on a given volume of tasks.
Computational Graphics Processing Unit (computational GPU; cGPU) (Графический процессор-вычислитель) – graphic processor-computer, GPU-computer, multi-core GPU used in hybrid supercomputers to perform parallel mathematical calculations; for example, one of the first GPUs in this category contains more than 3 billion transistors – 512 CUDA cores and up to 6 GB of memory. [[116 - Computational Graphics Processing Unit [Электронный ресурс] www.boston.co.uk URL: https://www.boston.co.uk/info/nvidia-kepler/what-is-gpu-computing.aspx (дата обращения 14.03.2022)]].
Computational humor (Вычислительный юмор) — A branch of computational linguistics and artificial intelligence which uses computers in humor research.
Computational intelligence (CI) (Вычислительный интеллект) — Usually refers to the ability of a computer to learn a specific task from data or experimental observation [].
Computational intelligence (CI) (Вычислительный интеллект) — Usually refers to the ability of a computer to learn a specific task from data or experimental observation.
Computational learning theory (COLT) (Теория вычислительного обучения) – In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms [[117 - Computational learning theory (COLT) [Электронный ресурс] www.semanticscholar.org URL: https://www.semanticscholar.org/topic/Computational-learning-theory/164025 (дата обращения 28.02.2022)]].
Computational linguistics (Компьютерная лингвистика) – An interdisciplinary field concerned with the statistical or rule-based modeling of natural language from a computational perspective, as well as the study of appropriate computational approaches to linguistic questions.
Computational mathematics (Вычислительная математика) — is the mathematical research in areas of science where computing plays an essential role.
Computational neuroscience (Also theoretical neuroscience or mathematical neuroscience) (Вычислительная нейробиология) – is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system.
Computational number theory (Also algorithmic number theory) (Вычислительная теория чисел) – The study of computational methods for researching and solving problems in number theory and arithmetic geometry, including algorithms for checking primality and numerical factorization, finding solutions to Diophantine equations, and explicit methods in arithmetic geometry. Computational number theory has applications to cryptography, including RSA, elliptic curve cryptography, and post-quantum cryptography, and is used to investigate the hypothesis and open problem of number theory, including the Riemann hypothesis, the Birch and Swinnerton-Dyer hypothesis, the ABC hypothesis, the modularity hypothesis, the Sato- Tate and explicit aspects of the Langlands program.
Computational problem (Вычислительная задача) – In theoretical computer science, a computational problem is a mathematical object representing a collection of questions that computers might be able to solve [[118 - Computational problem [Электронный ресурс] //cs.stackexchange.com URL: https://cs.stackexchange.com/questions/47757/computational-problem-definition (дата обращения 12.03.2022)]].
Computational statistics (Also statistical computing) (Вычислительная статистика) – Computational science is the application of computer science and software engineering principles to solving scientific problems. It involves the use of computing hardware, networking, algorithms, programming, databases and other domain-specific knowledge to design simulations of physical phenomena to run on computers. Computational science crosses disciplines and can even involve the humanities.
Computer engineering (Компьютерный инжиниринг) – technologies for digital modeling and design of objects and production processes throughout the life cycle.
