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Affective Computing
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While some core ideas in the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper on affective computing and her book ''Affective Computing'' published by MIT Press. One of the motivations for the research is the ability to give machines emotional intelligence, including to simulate empathy. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions. Areas Detecting and recognizing emotional information Detecting emotional information usually begins with passive sensors that capture data about the user's physical state or behavior without interp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Rosalind Picard
Rosalind Wright Picard (born May 17, 1962) is an American scholar and inventor who is Professor of Media Arts and Sciences at MIT, founder and director of the Affective Computing Research Group at the MIT Media Lab, and co-founder of the startups Affectiva and Empatica. She has received many recognitions for her research and inventions. In 2005, she was named a Fellow of the Institute of Electrical and Electronics Engineers for contributions to image and video analysis and affective computing. In 2019 she received one of the highest professional honors accorded an engineer, election to the National Academy of Engineering for her contributions on affective computing and wearable computing. In 2021 she was recognized as a Fellow of the ACM for contributions to physiological signal sensing for individual health and wellbeing. In 2021 she was elected to the National Academy of Inventors, which recognizes outstanding inventions that have made a tangible impact on quality of life, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Affect (psychology)
Affect, in psychology, refers to the underlying experience of feeling, emotion or mood. History The modern conception of affect developed in the 19th century with Wilhelm Wundt. The word comes from the German ''Gefühl'', meaning "feeling." A number of experiments have been conducted in the study of social and psychological affective preferences (i.e., what people like or dislike). Specific research has been done on preferences, attitudes, impression formation, and decision-making. This research contrasts findings with recognition memory (old-new judgments), allowing researchers to demonstrate reliable distinctions between the two. Affect-based judgments and cognitive processes have been examined with noted differences indicated, and some argue affect and cognition are under the control of separate and partially independent systems that can influence each other in a variety of ways ( Zajonc, 1980). Both affect and cognition may constitute independent sources of effects ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Marvin Minsky
Marvin Lee Minsky (August 9, 1927 – January 24, 2016) was an American cognitive and computer scientist concerned largely with research of artificial intelligence (AI), co-founder of the Massachusetts Institute of Technology's AI laboratory, and author of several texts concerning AI and philosophy. Minsky received many accolades and honors, including the 1969 Turing Award. Biography Marvin Lee Minsky was born in New York City, to an eye surgeon father, Henry, and to a mother, Fannie (Reiser), who was a Zionist activist. His family was Jewish. He attended the Ethical Culture Fieldston School and the Bronx High School of Science. He later attended Phillips Academy in Andover, Massachusetts. He then served in the US Navy from 1944 to 1945. He received a B.A. in mathematics from Harvard University in 1950 and a Ph.D. in mathematics from Princeton University in 1954. His doctoral dissertation was titled "Theory of neural-analog reinforcement systems and its application to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Decision Tree Learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making). General ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Support Vector Machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. N ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Gaussian Mixture Model
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information. Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). However, compositional models can be thought of as mixture models, wh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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K-nearest Neighbor Algorithm
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the ''k'' closest training examples in a data set. The output depends on whether ''k''-NN is used for classification or regression: :* In ''k-NN classification'', the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its ''k'' nearest neighbors (''k'' is a positive integer, typically small). If ''k'' = 1, then the object is simply assigned to the class of that single nearest neighbor. :* In ''k-NN regression'', the output is the property value for the object. This value is the average of the values of ''k'' nearest neighbors. If ''k'' = 1, then the output is simply assigned to the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Linear Classifier
In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector. Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables ( features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use. Definition If the input feature vector to the classifier is a real vector \vec x, then the output score is :y = f(\vec\cdot\vec) = f\left(\sum_j w_j x_j\right), where \vec w is a real vector of weights and ''f'' is a function that converts the dot product of the two vectors into the desired outp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Vector Space Model
Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers (such as index terms). It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System. Definitions Documents and queries are represented as vectors. :d_j = ( w_ ,w_ , \dotsc ,w_ ) :q = ( w_ ,w_ , \dotsc ,w_ ) Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is tf-idf weighting (see the example below). The definition of ''term'' depends on the application. Typically terms are single words, keywords, or longer phrases. If words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary (the number of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Knowledge Base
A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. Original usage of the term The original use of the term knowledge base was to describe one of the two sub-systems of an expert system. A knowledge-based system consists of a knowledge-base representing facts about the world and ways of reasoning about those facts to deduce new facts or highlight inconsistencies. Properties The term "knowledge-base" was coined to distinguish this form of knowledge store from the more common and widely used term ''database''. During the 1970s, virtually all large management information systems stored their data in some type of hierarchical or relational database. At this point in the history of information technology, the distinction between a database and a knowledge-base was clear and unambiguous. A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Database
In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spans formal techniques and practical considerations, including data modeling, efficient data representation and storage, query languages, security and privacy of sensitive data, and distributed computing issues, including supporting concurrent access and fault tolerance. A database management system (DBMS) is the software that interacts with end users, applications, and the database itself to capture and analyze the data. The DBMS software additionally encompasses the core facilities provided to administer the database. The sum total of the database, the DBMS and the associated applications can be referred to as a database system. Often the term "database" is also used loosely to refer to any of the DBMS, the database system or an appli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |