Artificial Intelligence: - Computer Science, Stony Brook University
... of machine language that uses known data sets to create predictions) algorithms to create classifiers from various object classes. Classifiers that are freshly created then accept input data and create new objects as well as class labels. These techniques are used in computer vision for many things ...
... of machine language that uses known data sets to create predictions) algorithms to create classifiers from various object classes. Classifiers that are freshly created then accept input data and create new objects as well as class labels. These techniques are used in computer vision for many things ...
Selective Data Acquisition for Machine Learning.
... direct the acquisition of various sorts of information. The first is to prefer to acquire information for which the current state of modeling is uncertain. The second is to acquire information that is estimated to be the most valuable to acquire. After expanding upon these two overarching notions, w ...
... direct the acquisition of various sorts of information. The first is to prefer to acquire information for which the current state of modeling is uncertain. The second is to acquire information that is estimated to be the most valuable to acquire. After expanding upon these two overarching notions, w ...
artificial intelligence and life in 2030
... their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when ...
... their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when ...
Continuous transformation learning of translation
... requires different weight vectors to be learned by neurons than are typically learned by competitive networks in which the patterns within a cluster overlap with each other. We show here how translation invariant representations can be learned in continuous transformation learning by the associative ...
... requires different weight vectors to be learned by neurons than are typically learned by competitive networks in which the patterns within a cluster overlap with each other. We show here how translation invariant representations can be learned in continuous transformation learning by the associative ...
Artificial Intelligence and Life in 2030
... their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when ...
... their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when ...
artificial intelligence and life in 2030
... their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when ...
... their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when ...
Reinforcement Learning and the Reward Engineering Principle
... For most concrete cases faced today—by Mars rovers, or by financial agents, for example—the reader should be able to devise ad hoc reward engineering methods that prevent some pathological dominance relationships from holding. However, the theoretical problem remains unsolved, and may rear its head ...
... For most concrete cases faced today—by Mars rovers, or by financial agents, for example—the reader should be able to devise ad hoc reward engineering methods that prevent some pathological dominance relationships from holding. However, the theoretical problem remains unsolved, and may rear its head ...
Reinforcement Learning in Real Time Strategy Games Case Study
... Chapter 6 we report the experimental results of our work, and finally in Chapter 7 we discuss the significance of our results, suggest future work directions, and draw some conclusions. ...
... Chapter 6 we report the experimental results of our work, and finally in Chapter 7 we discuss the significance of our results, suggest future work directions, and draw some conclusions. ...
Clustering Binary Data with Bernoulli Mixture Models
... statistical theory” (Marriott, 1974). Finite mixture models provide a convenient framework to model population heterogeneity and facilitate clustering (McLachlan and Peel, 2000). Heterogeneity in a population is reframed as arising from the pooling (or mixture) of a finite collection of relatively h ...
... statistical theory” (Marriott, 1974). Finite mixture models provide a convenient framework to model population heterogeneity and facilitate clustering (McLachlan and Peel, 2000). Heterogeneity in a population is reframed as arising from the pooling (or mixture) of a finite collection of relatively h ...
Consolidation
... difference lies in the types of interventions possible, which, in this case, include psychological interventions. This susceptibility to psychological intervention means that the cognitive system itself can impact the strength of learning. The implications of this fact are enormous; it means that th ...
... difference lies in the types of interventions possible, which, in this case, include psychological interventions. This susceptibility to psychological intervention means that the cognitive system itself can impact the strength of learning. The implications of this fact are enormous; it means that th ...
Data Splitting
... In machine learning, one of the main requirements is to build computational models with high prediction and generalization capabilities [Mitchell, 1997]. In the case of supervised learning, a computational model is trained to predict outputs of an unknown target function. The target function is repr ...
... In machine learning, one of the main requirements is to build computational models with high prediction and generalization capabilities [Mitchell, 1997]. In the case of supervised learning, a computational model is trained to predict outputs of an unknown target function. The target function is repr ...
S - GdR-IQFA
... • Learning time is externally measured, by the number of rounds it takes until rewarded actions are produced with high probability. ...
... • Learning time is externally measured, by the number of rounds it takes until rewarded actions are produced with high probability. ...
Machine Condition Monitoring Using Artificial Intelligence: The
... need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often common that the data becomes available in small batches over a period of t ...
... need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often common that the data becomes available in small batches over a period of t ...
Proceedings of 2014 BMI the Third International Conference on
... The Brain Works like Bridge-Islands with Modulation.............................................................................. ix The New Memory Technology to Support Brain-Like Computer .............................................................. x Brain-Inspired Multi-Anything Algorithms for ...
... The Brain Works like Bridge-Islands with Modulation.............................................................................. ix The New Memory Technology to Support Brain-Like Computer .............................................................. x Brain-Inspired Multi-Anything Algorithms for ...
Hybrid intelligent systems in petroleum reservoir characterization
... procedures and excellent predictive capabilities deployed by the techniques. A single overall technique that comes out of this approach of combining two or more existing techniques is called a hybrid system (Chandra and Yao 2006; Khashei et al. 2011). It is an approach that combines different theore ...
... procedures and excellent predictive capabilities deployed by the techniques. A single overall technique that comes out of this approach of combining two or more existing techniques is called a hybrid system (Chandra and Yao 2006; Khashei et al. 2011). It is an approach that combines different theore ...
Foundations of Artificial Intelligence
... field ...gentle revolutions have occurred in robotics, computer vision, machine learning (including neural networks), and knowledge representation. ...
... field ...gentle revolutions have occurred in robotics, computer vision, machine learning (including neural networks), and knowledge representation. ...
Learning logical definitions from relations
... Concept learning, which Hunt, Marin, and Stone (1966) describe succinctly as "[the] capacity to develop classification rules from experience" has long been a principal area of machine learning research. Supervised concept learning systems are supplied with information about several entities whose cl ...
... Concept learning, which Hunt, Marin, and Stone (1966) describe succinctly as "[the] capacity to develop classification rules from experience" has long been a principal area of machine learning research. Supervised concept learning systems are supplied with information about several entities whose cl ...
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.