High Performance Data mining by Genetic Neural Network
... or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on e ...
... or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on e ...
Alan Turing and the development of Artificial Intelligence
... grammed. The amount of work in the education we can assume, as a first approximation, to be much the same as for the human child. 4.1. The ab initio Machine Learning movement [1980s-1990s] During the 1970s the success of the expert systems movement (see Section 3.2) became increasingly stifled by th ...
... grammed. The amount of work in the education we can assume, as a first approximation, to be much the same as for the human child. 4.1. The ab initio Machine Learning movement [1980s-1990s] During the 1970s the success of the expert systems movement (see Section 3.2) became increasingly stifled by th ...
DATA MINING IN FINANCE AND ACCOUNTING: A - delab-auth
... difficult to humans to interpret the way NNs reach their decisions. However, algorithms have been proposed to extract comprehendible rules from NNs. Another criticism on NNs is that a number of parameters like the network topology must be defined empirically. It seems that NNs attract the interest o ...
... difficult to humans to interpret the way NNs reach their decisions. However, algorithms have been proposed to extract comprehendible rules from NNs. Another criticism on NNs is that a number of parameters like the network topology must be defined empirically. It seems that NNs attract the interest o ...
Deep Sparse Rectifier Neural Networks
... because the objective of the former is to obtain computationally efficient learners, that generalize well to new examples, whereas the objective of the latter is to abstract out neuroscientific data while obtaining explanations of the principles involved, providing predictions and guidance for futur ...
... because the objective of the former is to obtain computationally efficient learners, that generalize well to new examples, whereas the objective of the latter is to abstract out neuroscientific data while obtaining explanations of the principles involved, providing predictions and guidance for futur ...
Unsupervised Object Counting without Object Recognition
... popular image databases that include vehicles [28]–[31] and several hundred manually labeled images that came from our training data set. For the training of the supervised alternatives, manual vehicle-counting and labeling were used to create the labeled data, and they took several days to complete ...
... popular image databases that include vehicles [28]–[31] and several hundred manually labeled images that came from our training data set. For the training of the supervised alternatives, manual vehicle-counting and labeling were used to create the labeled data, and they took several days to complete ...
Parallel Data Analysis - DROPS
... allows efficient iterative processing, as data (once read from disk or other I/O) can be kept in the main memory by map tasks, and reused in subsequent computation phases (usually, each phase being triggered by new messages/data from the reducer). We evaluate this architecture and its ability to pro ...
... allows efficient iterative processing, as data (once read from disk or other I/O) can be kept in the main memory by map tasks, and reused in subsequent computation phases (usually, each phase being triggered by new messages/data from the reducer). We evaluate this architecture and its ability to pro ...
An Artificial Intelligence Neural Network based Crop Simulation
... algorithm. The model is evaluated by the credits for 120 applicants. The 120 data are separated in three groups: a “gwd credit” group, a ‘‘middle credit” group and a “had credit” group. The simulation shows that the neural network credit-risk evaluation model has higher classification accuracy compa ...
... algorithm. The model is evaluated by the credits for 120 applicants. The 120 data are separated in three groups: a “gwd credit” group, a ‘‘middle credit” group and a “had credit” group. The simulation shows that the neural network credit-risk evaluation model has higher classification accuracy compa ...
Collaboration Report
... appears to suit your needs down to the ground. You may not be able to do the whole thing but I think that Y2s could have a good go at the early parts. Helen agreed with this so I used it as my main focus. She also made reference to the bears so I decided to do this with the lower ability. Helen said ...
... appears to suit your needs down to the ground. You may not be able to do the whole thing but I think that Y2s could have a good go at the early parts. Helen agreed with this so I used it as my main focus. She also made reference to the bears so I decided to do this with the lower ability. Helen said ...
Classification and Prediction
... Bayesian classifiers are statistical classifiers. They can predict class membership probabilities, such as the probability that a given sample belongs to a particular class. Bayesian classification is based on Bayes theorem. Naive Bayesian Classifier is comparable in performance with decision tree a ...
... Bayesian classifiers are statistical classifiers. They can predict class membership probabilities, such as the probability that a given sample belongs to a particular class. Bayesian classification is based on Bayes theorem. Naive Bayesian Classifier is comparable in performance with decision tree a ...
CHS-Soar - AGI conferences
... (demonstrating domain independent learning in Soar) The Second Conference on Artificial General Intelligence, AGI-09 ...
... (demonstrating domain independent learning in Soar) The Second Conference on Artificial General Intelligence, AGI-09 ...
An Ensemble Method for Clustering
... All our experiments have been performed in R (Ihaka & Gentleman, 1996), a system for statistical computation and graphics, which implements the well-known Slanguage for statistics. R runs under a variety of Unix platforms (such as Linux) and under Windows95/98/ME/2000/NT. It is available freely via ...
... All our experiments have been performed in R (Ihaka & Gentleman, 1996), a system for statistical computation and graphics, which implements the well-known Slanguage for statistics. R runs under a variety of Unix platforms (such as Linux) and under Windows95/98/ME/2000/NT. It is available freely via ...
M.Tech (Full Time) – KNOWLEDGE ENGINEERING
... KNOWLEDGE BASED NEURAL COMPUTING Prerequisite CS0543 ...
... KNOWLEDGE BASED NEURAL COMPUTING Prerequisite CS0543 ...
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.