Predictive analytics encompasses a variety of techniques from statistics, data mining and game theory that analyze current and historical facts to make predictions about future events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics are also used in insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
Wednesday, October 21, 2009
Recurrent neural network
A recurrent neural network (RNN) is a class of neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior.
Recurrent neural networks must be approached differently from feedforward neural networks, both when analyzing their behavior and training them. Recurrent neural networks can also behave chaotically. Usually, dynamical systems theory is used to model and analyze them. While a feedforward network propagates data linearly from input to output, recurrent networks (RN) also propagate data from later processing stages to earlier stages.
Recurrent neural networks must be approached differently from feedforward neural networks, both when analyzing their behavior and training them. Recurrent neural networks can also behave chaotically. Usually, dynamical systems theory is used to model and analyze them. While a feedforward network propagates data linearly from input to output, recurrent networks (RN) also propagate data from later processing stages to earlier stages.
Cognitive science
Cognitive science can be defined as the study of mind or the study of thought. It embraces multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, linguistics, anthropology, sociology and biology. It relies on varying scientific methodology (e.g. behavioral experimentation, computational simulations, neuro-imaging, statistical analyses), and spans many levels of analysis of the mind (from low-level learning and decision mechanisms to high-level logic and planning, from neural circuitry to modular brain organization, etc.). The term cognitive science was coined by Christopher Longuet-Higgins in his 1973 commentary on the Lighthill report, which concerned the then-current state of Artificial Intelligence research. In the same decade, the journal Cognitive Science and the Cognitive Science Society were founded.Cognitive science differs from cognitive psychology in that algorithms that are intended to simulate human behavior are implemented or implementable on a computer.
Memristor
A memristor ("memory resistor") is any of various kinds of passive two-terminal circuit elements that maintain a functional relationship between the time integrals of current and voltage. This function, called memristance, is similar to variable resistance. Specifically engineered memristors provide controllable resistance, but such devices are not commercially available. Other devices like batteries and varistors have memristance, but it does not normally dominate their behavior. The definition of the memristor is based solely on fundamental circuit variables, similarly to the resistor, capacitor, and inductor. Unlike those three elements, which are allowed in linear time-invariant or LTI system theory, memristors are nonlinear and may be described by any of a variety of time-varying functions of net charge. There is no such thing as a generic memristor. Instead, each device implements a particular function, wherein either the integral of voltage determines the integral of current, or vice versa. A linear time-invariant memristor is simply a conventional resistor.Memristor theory was formulated and named by Leon Chua in a 1971 paper. Chua extrapolated the conceptual symmetry between the resistor, inductor, and capacitor, and inferred that the memristor is a similarly fundamental device. Other scientists had already used fixed nonlinear flux-charge relationships, but Chua's theory introduces generality.
Neuro-fuzzy
In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-fuzzy hybridization results in a hybrid intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the ability to solicit interpretable IF-THEN rules.
The strength of neuro-fuzzy systems involves two contradictory requirements in fuzzy modeling: interpretability versus accuracy. In practice, one of the two properties prevails. The neuro-fuzzy in fuzzy modeling research field is divided into two areas: linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model; and precise fuzzy modeling that is focused on accuracy, mainly the Takagi-Sugeno-Kang (TSK) model.
Although generally assumed to be the realization of a fuzzy system through connectionist networks, this term is also used to describe some other configurations including:
The strength of neuro-fuzzy systems involves two contradictory requirements in fuzzy modeling: interpretability versus accuracy. In practice, one of the two properties prevails. The neuro-fuzzy in fuzzy modeling research field is divided into two areas: linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model; and precise fuzzy modeling that is focused on accuracy, mainly the Takagi-Sugeno-Kang (TSK) model.
Although generally assumed to be the realization of a fuzzy system through connectionist networks, this term is also used to describe some other configurations including:
Cultured neuronal network
A cultured neuronal network is a cell culture of neurons that is used as a model to study the central nervous system, especially the brain. Often, cultured neuronal networks are connected to an input/output device such as a multi-electrode array (MEA), thus allowing two-way communication between the researcher and the network. This model has proved to be an invaluable tool to scientists studying the underlying principles behind neuronal learning, memory, plasticity, connectivity, and information processing.
Cultured neurons are often connected via computer to a real or simulated robotic component, creating a hybrot or animat, respectively. Researchers can then thoroughly study learning and plasticity in a realistic context, where the neuronal networks are able to interact with their environment and receive at least some artificial sensory feedback. One example of this can be seen in the Multielectrode Array Art (MEART) system developed by the Potter Research Group at the Georgia Institute of Technology in collaboration with the Symbi-oticA Research Group at the University of Western Australia. Another example can be seen in the neutrally controlled animat.
Cultured neurons are often connected via computer to a real or simulated robotic component, creating a hybrot or animat, respectively. Researchers can then thoroughly study learning and plasticity in a realistic context, where the neuronal networks are able to interact with their environment and receive at least some artificial sensory feedback. One example of this can be seen in the Multielectrode Array Art (MEART) system developed by the Potter Research Group at the Georgia Institute of Technology in collaboration with the Symbi-oticA Research Group at the University of Western Australia. Another example can be seen in the neutrally controlled animat.
Neural networks and neuroscience
Theoretical and computational neuroscience is the field concerned with the theoretical analysis and computational modeling of biological neural systems. Since neural systems are intimately related to cognitive processes and behaviour, the field is closely related to cognitive and behavioural modeling.
The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (biological neural network models) and theory (statistical learning theory and information theory).
The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (biological neural network models) and theory (statistical learning theory and information theory).
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