target="_blank" rel="nofollow" href="#note180" type="note">180.
Brain—computer interface (BCI), sometimes called a brain—machine interface (BMI), is a direct communication pathway between the brain’s electrical activity and an external device, most commonly a computer or robotic limb. Research on brain—computer interface began in the 1970s by Jacques Vidal at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The Vidal’s 1973 paper marks the first appearance of the expression brain—computer interface in scientific literature181.
Brain-inspired computing – calculations on brain-like structures, brain-like calculations using the principles of the brain (see also neurocomputing, neuromorphic engineering).
Branching factor in computing, tree data structures, and game theory, the number of children at each node, the outdegree. If this value is not uniform, an average branching factor can be calculated182,183.
Broadband refers to various high-capacity transmission technologies that transmit data, voice, and video across long distances and at high speeds. Common mediums of transmission include coaxial cables, fiber optic cables, and radio waves184.
Brute-force search (also exhaustive search or generate and test) is a very general problem-solving technique and algorithmic paradigm that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement185.
Bucketing – converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range186.
Byte – eight bits. A byte is simply a chunk of 8 ones and zeros. For example: 01000001 is a byte. A computer often works with groups of bits rather than individual bits and the smallest group of bits that a computer usually works with is a byte. A byte is equal to one column in a file written in character format187.
«C»
CAFFE is short for Convolutional Architecture for Fast Feature Embedding which is an open-source deep learning framework de- veloped in Berkeley AI Research. It supports many different deep learning architectures and GPU-based acceleration computation kernels188,189.
Calibration layer is a post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels190.
Candidate generation — the initial set of recommendations chosen by a recommendation system191.
Candidate sampling is a training-time optimization in which a probability is calculated for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For example, if we have an example labeled beagle and dog candidate sampling computes the predicted probabilities and corresponding loss terms for the beagle and dog class outputs in addition to a random subset of the remaining classes (cat, lollipop, fence). The idea is that the negative classes can learn from less frequent negative reinforcement as long as positive classes always get proper positive reinforcement, and this is indeed observed empirically. The motivation for candidate sampling is a computational efficiency win from not computing predictions for all negatives192.
Canonical Formats in information technology, canonicalization is the process of making something conform] with some specification… and is in an approved format. Canonicalization may sometimes mean generating canonical data from noncanonical data. Canonical formats are widely supported and considered to be optimal for long-term preservation193.
Capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization194,195.
Case-Based Reasoning (CBR) is a way to solve a new problem by using solutions to similar problems. It has been formalized to a process consisting of case retrieve, solution reuse, solution revise, and case retention196.
Categorical data — features having a discrete set of possible values. For example, consider a categorical feature named house style, which has a discrete set of three possible values: Tudor, ranch, colonial. By representing house style as categorical data, the model can learn the separate impacts of Tudor, ranch, and colonial on house price. Sometimes, values in the discrete set are mutually exclusive, and only one value can be applied to a given example. For example, a car maker categorical feature would probably permit only a single value (Toyota) per example. Other times, more than one value may be applicable. A single car could be painted more than one different color, so a car color categorical feature would likely permit a single example to have multiple values (for example, red and white). Categorical features are sometimes called discrete features. Contrast with numerical data197.
Center for Technological Competence is an organization that owns the results, tools for conducting fundamental research and platform solutions available to market participants to create applied solutions (products) on their basis. The Technology Competence Center can be a separate organization or be part of an application technology holding company198.
Central Processing Unit (CPU) is a von Neumann cyclic processor designed to execute complex computer programs199.
Centralized control is a process in which control signals are generated in a single control center and transmitted from it to numerous control objects200.
Centroid – the center of a cluster as determined by a k-means or k-median algorithm. For instance, if k is 3, then the k-means or k-median algorithm finds 3 centroids201.
Centroid-based clustering is a category of clustering algorithms that organizes data into nonhierarchical clusters. k-means is the most widely used centroid-based clustering algorithm. Contrast with hierarchical clustering algorithms202.
Character format
Brain—computer interface [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface (дата обращения: 07.07.2022)
Branching factor [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Branching_factor (дата обращения: 28.03.2023)
Коэффициент ветвления дерева [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Коэффициент_ветвления_(информатика) (дата обращения: 28.03.2023)
Broadband [Электронный ресурс] www.investopedia.com URL: https://www.investopedia.com/terms/b/broadband.asp (дата обращения: 07.07.2022)
Brute-force search [Электронный ресурс] https://spravochnick.ru URL: https://spravochnick.ru/informatika/algoritmizaciya/algoritm_polnogo_perebora/ (дата обращения: 07.02.2022)
Bucketing [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/bucketing (дата обращения: 29.06.2023)
Byte [Электронный ресурс] www.umich.edu (дата обращения: 07.07.2022) URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B
CAFFE [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Caffe_(software) (дата обращения: 02.07.2023)
Среда CAFFE (сверточная архитектура для быстрого внедрения функций) [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Caffe (дата обращения: 02.07.2023)
Calibration layer [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#iteration (дата обращения: 02.05.2023)
Candidate generation [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/recommendation/overview/candidate-generation (дата обращения: 10.01.2022)
Candidate sampling [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#candidate-sampling (дата обращения: 28.03.2023)
Canonical Formats [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (дата обращения: 07.07.2022)
Capsule neural network [Электронный ресурс] https://ru.what-this.com URL: https://ru.what-this.com/7202531/1/kapsulnaya-neyronnaya-set.html (дата обращения: 07.02.2022)
Capsule neural network [Электронный ресурс] https://neurohive.io URL: https://neurohive.io/ru/osnovy-data-science/kapsulnaja-nejronnaja-set-capsnet/ (дата обращения: 08.02.2022)
Case-Based Reasoning [Электронный ресурс] www.telusinternational.com URL: https://www.telusinternational.com/articles/50-beginner-ai-terms-you-should-know (дата обращения 15.01.2022)
Categorical data [Электронный ресурс] https://machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/understanding-feature-engineering-part-2-categorical-data-f54324193e63/ (дата обращения: 03.03.2022)
Центр технологических компетенций [Электронный ресурс] http://chesalov.com URL: http://chesalov.com/chesalov-index/ (дата обращения: 10.07.2023)
Central Processing Unit (CPU) [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Central_processing_unit (дата обращения: 10.07.2023)
Централизованное управление [Электронный ресурс] https://marketing.wikireading.ru URL: https://marketing.wikireading.ru/40372 (дата обращения: 10.07.2023)
Centroid [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#centroid (дата обращения: 10.07.2023)
Centroid-based clustering [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#centroid-based-clustering (дата обращения: 03.05.2023)
