1993 - KDnuggets
... in the prediction of protein secondary structure, where not only was the ILP method better in terms of predictive accuracy than alternative published methods, but perhaps more significantly, it yielded new domain knowledge. Still, much work remains to be done in handling noisy probabilistic concepts ...
... in the prediction of protein secondary structure, where not only was the ILP method better in terms of predictive accuracy than alternative published methods, but perhaps more significantly, it yielded new domain knowledge. Still, much work remains to be done in handling noisy probabilistic concepts ...
Artificial Intelligence in the Open World
... probabilistic and logic-based approaches that make decisions under limited time, I’ve often thought about Simon’s fanciful image of the ant interacting with the complex environment at the beach, wandering among the hills and nooks and crannies, in thinking about building computational intelligences ...
... probabilistic and logic-based approaches that make decisions under limited time, I’ve often thought about Simon’s fanciful image of the ant interacting with the complex environment at the beach, wandering among the hills and nooks and crannies, in thinking about building computational intelligences ...
Improving Learning Performance Through Rational ... Jonathan Gratch*, Steve Chien+, and ...
... processing it. Usually such factors are addressed by informal or intuitive judgements rather than a rational analysis of the costs and benefits of alternative learning operations. There is a significant learning cost in many diverse application areas. In speed-up learning there is substantial cost a ...
... processing it. Usually such factors are addressed by informal or intuitive judgements rather than a rational analysis of the costs and benefits of alternative learning operations. There is a significant learning cost in many diverse application areas. In speed-up learning there is substantial cost a ...
machine intelligence
... The idea that an artificial brain could compete with that of humans has been around for many years. In 2016, it’s exactly sixty years ago that computer scientist John McCarthy suggested to work on Artificial Intelligence (AI) with ten men for two summer months on the campus of Dartmouth College. An ...
... The idea that an artificial brain could compete with that of humans has been around for many years. In 2016, it’s exactly sixty years ago that computer scientist John McCarthy suggested to work on Artificial Intelligence (AI) with ten men for two summer months on the campus of Dartmouth College. An ...
Simple Algorithmic Theory of Subjective Beauty, Novelty
... storing raw data, and that each synapse can store at most 6 bits, there is still enough capacity to encode the lifelong sensory input stream with a rate of roughly 105 bits/s, comparable to the demands of a movie with reasonable resolution. The storage capacity of affordable technical systems will so ...
... storing raw data, and that each synapse can store at most 6 bits, there is still enough capacity to encode the lifelong sensory input stream with a rate of roughly 105 bits/s, comparable to the demands of a movie with reasonable resolution. The storage capacity of affordable technical systems will so ...
IS 687 Transaction Mining / Fraud Detection Course Syllabus Fall
... Fall 2014 Description of Course: In recent years, financial fraud, including credit card fraud, corporate fraud and money laundering, has attracted a great deal of concern and attention. Data mining plays an important role in fraud detection and is known as a process that uses statistical, mathemati ...
... Fall 2014 Description of Course: In recent years, financial fraud, including credit card fraud, corporate fraud and money laundering, has attracted a great deal of concern and attention. Data mining plays an important role in fraud detection and is known as a process that uses statistical, mathemati ...
Intelligent Systems - Teaching-WIKI
... often provides better estimates of generalization error at the cost of even more computing time. • No matter which method is applied, the estimate of the generalization error of the best network will be optimistic. • If several networks are trained using one data set, and a second (validation set) i ...
... often provides better estimates of generalization error at the cost of even more computing time. • No matter which method is applied, the estimate of the generalization error of the best network will be optimistic. • If several networks are trained using one data set, and a second (validation set) i ...
Text Mining Techniques for Leveraging Positively Labeled Data
... then effectively mislabeled as negative. By introducing such an artificial supplement to the negative training set we are not only certain that the negative set contains mislabeled positive examples, but we know exactly which ones they are. Our goal is to automatically identify these mislabeled docu ...
... then effectively mislabeled as negative. By introducing such an artificial supplement to the negative training set we are not only certain that the negative set contains mislabeled positive examples, but we know exactly which ones they are. Our goal is to automatically identify these mislabeled docu ...
all publications as Word document
... Symposium on Computer Science in Sports, Loughborough, UK, pp.119-127, ISBN: 978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7_15. Chung, P, Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 97 ...
... Symposium on Computer Science in Sports, Loughborough, UK, pp.119-127, ISBN: 978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7_15. Chung, P, Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 97 ...
Lecture 7A
... Extraction by Sliding Window GRAND CHALLENGES FOR MACHINE LEARNING Jaime Carbonell School of Computer Science Carnegie Mellon University ...
... Extraction by Sliding Window GRAND CHALLENGES FOR MACHINE LEARNING Jaime Carbonell School of Computer Science Carnegie Mellon University ...
Observational Learning Based on Models of - FORTH-ICS
... SI, SPL, AIPmotor, VIP and MT, and is assigned two tasks:(i) to form the neural codes that represent the motion of the fingers of our cognitive agent and (ii) to build a correspondence between the agent’s and the demonstrator’s actions (through the VIPSPL circuit). The process that allows the format ...
... SI, SPL, AIPmotor, VIP and MT, and is assigned two tasks:(i) to form the neural codes that represent the motion of the fingers of our cognitive agent and (ii) to build a correspondence between the agent’s and the demonstrator’s actions (through the VIPSPL circuit). The process that allows the format ...
The Rise of the Machines: How Chinese Executives Think about
... To better understand the potential impact that AI is likely to have on companies in traditional industries in China, we recently conducted a survey of 80 companies. Of the companies we surveyed, 60 are in traditional sectors such as retailing, heavy industry, and construction. We also surveyed 20 A ...
... To better understand the potential impact that AI is likely to have on companies in traditional industries in China, we recently conducted a survey of 80 companies. Of the companies we surveyed, 60 are in traditional sectors such as retailing, heavy industry, and construction. We also surveyed 20 A ...
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.