Prediction of maximum surface settlement caused by earth pressure
... can be estimated using empirical [1-5], analytical [6-11], and numerical methods [12-15]. Indeed, the amount of maximum surface settlement (MSS) is a complex function of many geotechnical and geometrical parameters. Since the empirical and analytical approaches have mostly been developed on the basi ...
... can be estimated using empirical [1-5], analytical [6-11], and numerical methods [12-15]. Indeed, the amount of maximum surface settlement (MSS) is a complex function of many geotechnical and geometrical parameters. Since the empirical and analytical approaches have mostly been developed on the basi ...
the file
... Cascade Correlation Neural Network Artificial neural networks are the combination of artificial neurons After testing and analysing various neural networks we found that the CCNN is the best for the application domain under consideration. The CCNN is a new architecture and is a generative, fe ...
... Cascade Correlation Neural Network Artificial neural networks are the combination of artificial neurons After testing and analysing various neural networks we found that the CCNN is the best for the application domain under consideration. The CCNN is a new architecture and is a generative, fe ...
Towards General AI: What we can learn from Human Learning
... The dominant mechanism for semantic learning is connectivity in the learning environment. The dominant mechanism for phonological learning is connectivity of known vocabulary words. ...
... The dominant mechanism for semantic learning is connectivity in the learning environment. The dominant mechanism for phonological learning is connectivity of known vocabulary words. ...
Unsupervised Learning What is clustering for?
... classification by statisticians and sorting by psychologists and segmentation by people in marketing • Organizing data into classes such that there is • high intra-class similarity • low inter-class similarity ...
... classification by statisticians and sorting by psychologists and segmentation by people in marketing • Organizing data into classes such that there is • high intra-class similarity • low inter-class similarity ...
Multiagent Learning: Basics, Challenges, and
... and joint action learners (Littman 1994, Claus and Boutilier 1998). In these approaches learning happens in the product space of the set of states and action sets of the different agents. Such approaches experience difficulties with large state-action spaces when the number of agents, states, and ac ...
... and joint action learners (Littman 1994, Claus and Boutilier 1998). In these approaches learning happens in the product space of the set of states and action sets of the different agents. Such approaches experience difficulties with large state-action spaces when the number of agents, states, and ac ...
A.I. in Power Systems Alarm Processing
... Language Processing, Machine Learning and Information Visualisation methods process the non-critical alarms generated by a HVPS. In our novel approach, the edit distance of alarm messages are related to the same device and location; Conditional Random Fields to recognise the named entities (e.g., de ...
... Language Processing, Machine Learning and Information Visualisation methods process the non-critical alarms generated by a HVPS. In our novel approach, the edit distance of alarm messages are related to the same device and location; Conditional Random Fields to recognise the named entities (e.g., de ...
Dr. Eick`s Introduction to AI
... • Computer can work in environment that are unsuitable for human beings. • If computers control everything --- who controls the computers? • If computers are intelligent what civil rights should be given to computers? • If computers can perform most of our work; what should the human beings do? • On ...
... • Computer can work in environment that are unsuitable for human beings. • If computers control everything --- who controls the computers? • If computers are intelligent what civil rights should be given to computers? • If computers can perform most of our work; what should the human beings do? • On ...
An Efficient Learning Procedure for Deep Boltzmann Machines
... 1. The performance is comparable with the best other single models, such as probabilistic matrix factorization. By averaging many models it is possible to do better and the two systems with the best performance on Netflix both use multiple RBMs among the many models that are averaged. ...
... 1. The performance is comparable with the best other single models, such as probabilistic matrix factorization. By averaging many models it is possible to do better and the two systems with the best performance on Netflix both use multiple RBMs among the many models that are averaged. ...
the full pdf program here - CDAR
... Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a typically large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and calling a traditional graph algorithm; but more interesting are locallybiased gra ...
... Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a typically large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and calling a traditional graph algorithm; but more interesting are locallybiased gra ...
Consolidation of motor memory
... entirely independently of the cerebellum. For example, in fear conditioning, the ability of a rat to recall the importance of an acoustic cue depends on the integrity of the interpositus for w96 h after initial training [20]. After that interval, disruption of the interpositus has no effect on recal ...
... entirely independently of the cerebellum. For example, in fear conditioning, the ability of a rat to recall the importance of an acoustic cue depends on the integrity of the interpositus for w96 h after initial training [20]. After that interval, disruption of the interpositus has no effect on recal ...
Representing Probabilistic Rules with Networks of
... Backgammon by playing against itself without a supervisor. TD-Gammon’s weights contain a tremendous amount of useful information. Currently there are basically only two ways to understand the functionality of the network: by plotting patterns of weight values or by gathering statistics of the networ ...
... Backgammon by playing against itself without a supervisor. TD-Gammon’s weights contain a tremendous amount of useful information. Currently there are basically only two ways to understand the functionality of the network: by plotting patterns of weight values or by gathering statistics of the networ ...
artificial intelligence in the real world
... (through a set of techniques called “deep learning”), and independently responded to Mr Lee’s moves. Machine learning, a subcategory of AI techniques which automate the learning process through algorithms and the super-powered analysis of data, has been around since the 1950s. Business applications ...
... (through a set of techniques called “deep learning”), and independently responded to Mr Lee’s moves. Machine learning, a subcategory of AI techniques which automate the learning process through algorithms and the super-powered analysis of data, has been around since the 1950s. Business applications ...
Behavior-based robotics as a tool for synthesis of artificial behavior
... science. Its dual goals are: (1) to develop methods for controlling artificial systems, ranging from physical robots to simulated ones and other autonomous software agents; and (2) to use robotics to model and understand biological systems more fully, typically, animals ranging from insects to human ...
... science. Its dual goals are: (1) to develop methods for controlling artificial systems, ranging from physical robots to simulated ones and other autonomous software agents; and (2) to use robotics to model and understand biological systems more fully, typically, animals ranging from insects to human ...
Preference Learning: An Introduction
... information provided as an input to the learning system. Roughly speaking, preference learning is about inducing predictive preference models from empirical data. In the literature on choice and decision theory, two main approaches to modeling preferences can be found, namely in terms of utility fun ...
... information provided as an input to the learning system. Roughly speaking, preference learning is about inducing predictive preference models from empirical data. In the literature on choice and decision theory, two main approaches to modeling preferences can be found, namely in terms of utility fun ...
Tech_Trends_Knowledge Based_Crowe_FINAL
... Assimilating the vast amount of information academics and others publish is a daunting task for students and faculty, especially in the distance learning environment. Using sources such as online libraries, Google Scholar, and other web-based sources can be challenging. Natural Language Processing ( ...
... Assimilating the vast amount of information academics and others publish is a daunting task for students and faculty, especially in the distance learning environment. Using sources such as online libraries, Google Scholar, and other web-based sources can be challenging. Natural Language Processing ( ...
Survey on Remotely Sensed Image Classification
... maps algorithm. All these algorithms are evaluated as per the following factors: number of clusters, size of dataset, type of dataset and type of software used. FSVM is used to enhance the SVM in reducing the effect of outliers and noises in data points and is suitable for applications, in which dat ...
... maps algorithm. All these algorithms are evaluated as per the following factors: number of clusters, size of dataset, type of dataset and type of software used. FSVM is used to enhance the SVM in reducing the effect of outliers and noises in data points and is suitable for applications, in which dat ...
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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.