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Neural Modeling Fields
Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS). This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of the Cognition, mind's mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, Heterarchy, hetero-hierarchical system. At each level in NMF there are Schema (psychology), concept-models encapsulating the knowledge; they generate so-called Bottom–up and top–down design, top-down signals, interacting with input, bottom-up signals. These interactions are governed by Dynamical system, dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Heterarchy
A heterarchy is a system of organization where the elements of the organization are unranked (non- hierarchical) or where they possess the potential to be ranked a number of different ways. Definitions of the term vary among the disciplines: in social and information sciences, heterarchies are networks of elements in which each element shares the same "horizontal" position of power and authority, each playing a theoretically equal role. In biological taxonomy, however, the requisite features of heterarchy involve, for example, a species sharing, with a species in a different family, a common ancestor which it does not share with members of its own family. This is theoretically possible under principles of "horizontal gene transfer". A heterarchy may be orthogonal to a hierarchy, subsumed to a hierarchy, or it may contain hierarchies; the two kinds of structure are not mutually exclusive. In fact, each level in a hierarchical system is composed of a potentially heterarchical group. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Perlovsky
Leonid Perlovsky is an Affiliated Research Professor at Northeastern University. His research involves cognitive algorithms and modeling of evolution of languages and cultures. He served as professor at Novosibirsk State University and New York University, and participated as a principal in commercial startups developing tools for text understanding, biotechnology, and financial predictions. He has published more than 320 papers and 10 book chapters and authored three books, including ''Neural Networks and Intellect'', Oxford University Press, 2000 (currently in the 3rd printing) and two books with Springer in 2007. He serves as Associate Editor for ''IEEE Transactions on Neural Networks'', Editor-at-Large for '' New Mathematics and Natural Computation'' and Editor-in-Chief for '' Physics of Life Reviews''. He has received national and international awards including the IEEE Distinguished Member of Boston Section Award 2005; the US AFRL Charles Ryan Memorial Award for Basic Researc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Akaike Information Criterion
The Akaike information criterion (AIC) is an estimator of prediction error and thereby relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection. AIC is founded on information theory. When a statistical model is used to represent the process that generated the data, the representation will almost never be exact; so some information will be lost by using the model to represent the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher the quality of that model. In estimating the amount of information lost by a model, AIC deals with the trade-off between the goodness of fit of the model and the simplicity of the model. In other words, AIC deals with both the risk of overfitting and the risk of underfitting. The Akaike information crite ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Penalty Method
In mathematical optimization, penalty methods are a certain class of algorithms for solving constrained optimization problems. A penalty method replaces a constrained optimization problem by a series of unconstrained problems whose solutions ideally converge to the solution of the original constrained problem. The unconstrained problems are formed by adding a term, called a penalty function, to the objective function that consists of a ''penalty parameter'' multiplied by a measure of violation of the constraints. The measure of violation is nonzero when the constraints are violated and is zero in the region where constraints are not violated. Description Let us say we are solving the following constrained problem: : \min_x f(\mathbf x) subject to : c_i(\mathbf x) \le 0 ~\forall i \in I. This problem can be solved as a series of unconstrained minimization problems : \min f_p (\mathbf x) := f (\mathbf x) + p ~ \sum_ ~ g(c_i(\mathbf x)) where : g(c_i(\mathbf x))=\max(0,c_ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Similarity Measure
In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of distance metrics: they take on large values for similar objects and either zero or a negative value for very dissimilar objects. Though, in more broad terms, a similarity function may also satisfy metric axioms. Cosine similarity is a commonly used similarity measure for real-valued vectors, used in (among other fields) information retrieval to score the similarity of documents in the vector space model. In machine learning, common kernel functions such as the RBF kernel can be viewed as similarity functions. Use of different similarity measure formulas Different types of similarity measures exist for various types of objects, depending on the objects being compared. For each type of object there ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Priming (psychology)
Priming is a concept in psychology and psycholinguistics to describe how exposure to one stimulus may influence a response to a subsequent stimulus, without conscious guidance or intention. The priming effect is the positive or negative effect of a rapidly presented stimulus (priming stimulus) on the processing of a second stimulus (target stimulus) that appears shortly after. Generally speaking, the generation of priming effect depends on the existence of some positive or negative relationship between priming and target stimuli. For example, the word ''nurse'' might be recognized more quickly following the word ''doctor'' than following the word ''bread''. Priming can be perceptual, associative, repetitive, positive, negative, affective, semantic, or conceptual. Priming effects involve word recognition, semantic processing, attention, unconscious processing, and many other issues, and are related to differences in various writing systems. How quickly this effect occurs is conte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aesthetic Emotions
Aesthetic emotions are emotions that are felt during aesthetic activity or appreciation. These emotions may be of the everyday variety (such as fear, wonder or sympathy) or may be specific to aesthetic contexts. Examples of the latter include the sublime, the beautiful, and the kitsch. In each of these respects, the emotion usually constitutes only a part of the overall aesthetic experience, but may play a more or less definitive function for that state. Types Visual arts and film The relation between aesthetic emotions and other emotions is traditionally said to rely on the disinterestedness of the aesthetic experience (see Kant especially). Aesthetic emotions do not motivate practical behaviours in the way that other emotions do (such as how fear motivates avoidance behaviours). The capacity of artworks to arouse emotions such as fear is a subject of philosophical and psychological research. [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dynamical System
In mathematics, a dynamical system is a system in which a Function (mathematics), function describes the time dependence of a Point (geometry), point in an ambient space, such as in a parametric curve. Examples include the mathematical models that describe the swinging of a clock pendulum, fluid dynamics, the flow of water in a pipe, the Brownian motion, random motion of particles in the air, and population dynamics, the number of fish each springtime in a lake. The most general definition unifies several concepts in mathematics such as ordinary differential equations and ergodic theory by allowing different choices of the space and how time is measured. Time can be measured by integers, by real number, real or complex numbers or can be a more general algebraic object, losing the memory of its physical origin, and the space may be a manifold or simply a Set (mathematics), set, without the need of a Differentiability, smooth space-time structure defined on it. At any given time, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bottom–up And Top–down Design
Bottom-up and top-down are strategies of composition and decomposition in fields as diverse as information processing and ordering knowledge, software, humanistic and scientific theories (see systemics), and management and organization. In practice they can be seen as a style of thinking, teaching, or leadership. A top-down approach (also known as ''stepwise design'' and stepwise refinement and in some cases used as a synonym of ''decomposition'') is essentially the breaking down of a system to gain insight into its compositional subsystems in a reverse engineering fashion. In a top-down approach an overview of the system is formulated, specifying, but not detailing, any first-level subsystems. Each subsystem is then refined in yet greater detail, sometimes in many additional subsystem levels, until the entire specification is reduced to base elements. A top-down model is often specified with the assistance of black boxes, which makes it easier to manipulate. However, black bo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Schema (psychology)
In psychology and cognitive science, a schema (: schemata or schemas) describes a pattern of thought or behavior that organizes categories of information and the relationships among them. It can also be described as a mental structure of preconceived ideas, a framework representing some aspect of the world, or a system of organizing and perceiving new information, such as a mental schema or conceptual model. Schemata influence attention and the absorption of new knowledge: people are more likely to notice things that fit into their schema, while re-interpreting contradictions to the schema as exceptions or distorting them to fit. Schemata have a tendency to remain unchanged, even in the face of contradictory information. Schemata can help in understanding the world and the rapidly changing environment. People can organize new perceptions into schemata quickly as most situations do not require complex thought when using schema, since automatic thought is all that is required. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
