Making Music with AI: Some examples
... real piano performers. The outputs of the neural networks express time and loudness deviations. These neural networks extend the standard feed-forward network trained with the back propagation algorithm with feedback connections from the output neurons to the input neurons. We can see that, except f ...
... real piano performers. The outputs of the neural networks express time and loudness deviations. These neural networks extend the standard feed-forward network trained with the back propagation algorithm with feedback connections from the output neurons to the input neurons. We can see that, except f ...
Associative learning signals in the brain
... including rabbits, rats, and primates. This review will show that similar patterns of associative learning signals have been reported across species, though the most thorough description to date has been done in the non-human primate model systems. The second related question concerns the other brai ...
... including rabbits, rats, and primates. This review will show that similar patterns of associative learning signals have been reported across species, though the most thorough description to date has been done in the non-human primate model systems. The second related question concerns the other brai ...
A bibliography of the intersection of genetic search and artificial
... This is a fairly informal bibliography of work relating artificial neural networks (ANNs) and genetic search. It is a collection of books, papers, presentations, and reports which I've come across in the course of pursuing my interest in using genetic search techniques for the design of ANNs and in ...
... This is a fairly informal bibliography of work relating artificial neural networks (ANNs) and genetic search. It is a collection of books, papers, presentations, and reports which I've come across in the course of pursuing my interest in using genetic search techniques for the design of ANNs and in ...
toward memory-based reasoning - Computer Science, Columbia
... Memory-based reasoning degrades gracefully when it cannot come up with a definitive answer to a problem: It may respond that no answer is possible, give one or more plausible answers, or ask for more information. BACKGROUND: AI PARADIGMS Memory-based reasoning is related to a number of other subfiel ...
... Memory-based reasoning degrades gracefully when it cannot come up with a definitive answer to a problem: It may respond that no answer is possible, give one or more plausible answers, or ask for more information. BACKGROUND: AI PARADIGMS Memory-based reasoning is related to a number of other subfiel ...
the application of artificial intelligence methods in heat - QRC
... programming, as well as the expert systems in predicting the steel properties and determination of heat treatment process parameters has been performed at the Department for Materials of FMENA. This paper presents the short overview of applied methods and results in predicting different properties o ...
... programming, as well as the expert systems in predicting the steel properties and determination of heat treatment process parameters has been performed at the Department for Materials of FMENA. This paper presents the short overview of applied methods and results in predicting different properties o ...
Prediction of Base Shear for Three Dimensional RC
... of the structure. Thus the method is more performancebased than conventional strength-based approach. Artificial neural networks (ANN)1 have emerged as a computationally powerful tool in artificial intelligence with the potential of mapping an unknown nonlinear relationship between the given set of ...
... of the structure. Thus the method is more performancebased than conventional strength-based approach. Artificial neural networks (ANN)1 have emerged as a computationally powerful tool in artificial intelligence with the potential of mapping an unknown nonlinear relationship between the given set of ...
An Algorithm for Fast Convergence in Training Neural Networks
... In the error backpropagation algorithm, the weight updates are proportional to the error propagating from the output through the derivatives of activation function and through the weights. This is a consequence of using the steepest descent method for calculating the weight adjustments. Convergence ...
... In the error backpropagation algorithm, the weight updates are proportional to the error propagating from the output through the derivatives of activation function and through the weights. This is a consequence of using the steepest descent method for calculating the weight adjustments. Convergence ...
Artificial Intelligence techniques: An introduction to their use for
... and action parts, if and then and are fed to an inference engine, which has a working memory of information about the problem, a pattern matcher and a rule applier. The pattern matcher refers to the working memory to decide which rules are relevant, then the rule applier chooses what rule to apply. ...
... and action parts, if and then and are fed to an inference engine, which has a working memory of information about the problem, a pattern matcher and a rule applier. The pattern matcher refers to the working memory to decide which rules are relevant, then the rule applier chooses what rule to apply. ...
modeling dynamical systems by means of dynamic bayesian networks
... is very rare in the literature, probably because of the availability of both theoretical tools (algorithms) and practical (availability of software). In many cases, taking into consideration only the first–order dependences is probably sufficient. However, there is a possibility that some phenomena ...
... is very rare in the literature, probably because of the availability of both theoretical tools (algorithms) and practical (availability of software). In many cases, taking into consideration only the first–order dependences is probably sufficient. However, there is a possibility that some phenomena ...
ICT619 Intelligent Systems
... Testing by weight analysis Weights entering and exiting nodes analysed for relatively small and large values In case of significant errors detected in testing, ...
... Testing by weight analysis Weights entering and exiting nodes analysed for relatively small and large values In case of significant errors detected in testing, ...
T R ECHNICAL ESEARCH
... where the last equality comes from diverging-node-case of d-separation definition, as shown in section 2.2. Here, λyj xi (xi ) is called the λ-message that carries the evidential information from node Xi ’s direct child Yj , with its value as yj . • Calculation of µ-message (top-down propagation ): ...
... where the last equality comes from diverging-node-case of d-separation definition, as shown in section 2.2. Here, λyj xi (xi ) is called the λ-message that carries the evidential information from node Xi ’s direct child Yj , with its value as yj . • Calculation of µ-message (top-down propagation ): ...
Neural Network Applications in Stock Market Predictions
... According to many authors, NN methodology underestimates the design of NN architecture (topology), and methods of training, testing, evaluating, and implementing the network [13]. Since the data regarding the evaluation and implementation phase were not available in all analyzed articles, the paper ...
... According to many authors, NN methodology underestimates the design of NN architecture (topology), and methods of training, testing, evaluating, and implementing the network [13]. Since the data regarding the evaluation and implementation phase were not available in all analyzed articles, the paper ...
Artificial Intelligence - University of Regina
... • Derive the whole mathematics through formal operations on a collection of axioms [Whitehead & Russell 1950] – Axioms and theorems treated as strings of characters. – Theorem proof using well defined rules for manipulating ...
... • Derive the whole mathematics through formal operations on a collection of axioms [Whitehead & Russell 1950] – Axioms and theorems treated as strings of characters. – Theorem proof using well defined rules for manipulating ...
Modeling Estuarine Salinity Using Artificial Neural Networks
... my junior year with a publication from Ecological Modeling on ANNs. Although the paper admittedly confused me at first, it quickly pulled me in with the ANN’s amazing ability to imitate the neurons in a human brain with a complicated mathematical “learning” process. Because I had just finished takin ...
... my junior year with a publication from Ecological Modeling on ANNs. Although the paper admittedly confused me at first, it quickly pulled me in with the ANN’s amazing ability to imitate the neurons in a human brain with a complicated mathematical “learning” process. Because I had just finished takin ...
Probabilistic Inductive Logic Programming
... programming system because Bayesian logic programs combine Bayesian networks [36], which represent probability distributions over propositional interpretations, with definite clause logic. Furthermore, Bayesian logic programs have already been employed for learning. The idea underlying Bayesian logi ...
... programming system because Bayesian logic programs combine Bayesian networks [36], which represent probability distributions over propositional interpretations, with definite clause logic. Furthermore, Bayesian logic programs have already been employed for learning. The idea underlying Bayesian logi ...
Classification of Deforestation Factors Using Data Mining
... Knowledge discovery in databases is the nontrivial process of identifying valid, novel, useful and ultimately understandable patterns in data [7]. The process of automatic classification based on data patterns obtained from data set is referred as Data mining [5]. Classification is one of the data m ...
... Knowledge discovery in databases is the nontrivial process of identifying valid, novel, useful and ultimately understandable patterns in data [7]. The process of automatic classification based on data patterns obtained from data set is referred as Data mining [5]. Classification is one of the data m ...
Supervised and unsupervised learning.
... be known, and a suitable penalty function W : K × D → R must be provided. Non-Bayesian decision theory studies tasks for which some of the above information is not available. In practical applications, typically, none of the probabilities are known! The designer is only provided with the training (m ...
... be known, and a suitable penalty function W : K × D → R must be provided. Non-Bayesian decision theory studies tasks for which some of the above information is not available. In practical applications, typically, none of the probabilities are known! The designer is only provided with the training (m ...
Machine learning
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition ""can be viewed as two facets ofthe same field.""When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.