Notes 1: Introduction to Artificial Intelligence
... – we could teach it lots of rules about what to do – or we could let it drive and steer it back on course when it heads for the embankment • systems like this are under development (e.g., Daimler Benz) • e.g., RALPH at CMU – in mid 90’s it drove 98% of the way from Pittsburgh to San Diego without an ...
... – we could teach it lots of rules about what to do – or we could let it drive and steer it back on course when it heads for the embankment • systems like this are under development (e.g., Daimler Benz) • e.g., RALPH at CMU – in mid 90’s it drove 98% of the way from Pittsburgh to San Diego without an ...
Ensemble Learning Techniques for Structured
... Skier Days Prediction." Expert Systems with Applications 41(4, Part 1): 1176-1188. The work presented in Chapter 3 is published as: King, M. A., A. S. Abrahams and C. T. Ragsdale (2015). "Ensemble Learning Methods for Payper-click Campaign Management." Expert Systems with Applications 42(10): 4818-4 ...
... Skier Days Prediction." Expert Systems with Applications 41(4, Part 1): 1176-1188. The work presented in Chapter 3 is published as: King, M. A., A. S. Abrahams and C. T. Ragsdale (2015). "Ensemble Learning Methods for Payper-click Campaign Management." Expert Systems with Applications 42(10): 4818-4 ...
IV. Model Application: the UAV Autonomous Learning in Unknown
... recent years since they provide new opportunities to achieve the goal of General Intelligence. The neural network architecture of the brain supports the realization of cognitive behaviors at multiple scales. Although spiking neural networks have been adopted for cognitive behavior simulation and cre ...
... recent years since they provide new opportunities to achieve the goal of General Intelligence. The neural network architecture of the brain supports the realization of cognitive behaviors at multiple scales. Although spiking neural networks have been adopted for cognitive behavior simulation and cre ...
Evolutionary Optimization of Radial Basis Function Classifiers for
... 3) Feature selection for RBF networks by means of EA is investigated in only five of the publications: [34] and [35] describe class-dependent feature selection by masking of features; other examples are [47], [48], and [72]. 4) Rule extraction from trained networks is shown in [43]. 5) The combinati ...
... 3) Feature selection for RBF networks by means of EA is investigated in only five of the publications: [34] and [35] describe class-dependent feature selection by masking of features; other examples are [47], [48], and [72]. 4) Rule extraction from trained networks is shown in [43]. 5) The combinati ...
Neural constraints on learning
... Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others1,2, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrai ...
... Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others1,2, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrai ...
Literature Review on Feature Selection Methods for High
... In the feature subset-based method, the features are combined as possible combinations of feature subsets using any one of the searching strategies. Then, the feature subsets are evaluated using any one of the statistical measures or the supervised learning algorithms to observe the significance of ...
... In the feature subset-based method, the features are combined as possible combinations of feature subsets using any one of the searching strategies. Then, the feature subsets are evaluated using any one of the statistical measures or the supervised learning algorithms to observe the significance of ...
Welcome to IJCAI 2015!
... International Joint Conference on AI (IJCAI-15). The two years since IJCAI-13 in Beijing have been a tremendously exciting time for AI. Scarcely a week has passed without news items about new advances in AI research, or exciting new applications for AI research, in areas ranging from autonomous vehi ...
... International Joint Conference on AI (IJCAI-15). The two years since IJCAI-13 in Beijing have been a tremendously exciting time for AI. Scarcely a week has passed without news items about new advances in AI research, or exciting new applications for AI research, in areas ranging from autonomous vehi ...
Exploring the Complex Interplay between AI and Consciousness
... would be easily integrated into old. Several tasks could be learned concurrently with transfer of knowledge to new tasks. It is important to acknowledge that not all artificial agents require consciousness to achieve their goals. In fact agents involved in relatively simple tasks in restricted domai ...
... would be easily integrated into old. Several tasks could be learned concurrently with transfer of knowledge to new tasks. It is important to acknowledge that not all artificial agents require consciousness to achieve their goals. In fact agents involved in relatively simple tasks in restricted domai ...
Artificial Intelligence: The Ultimate Technological Disruption Ascends
... Within AI, the progress of the field has caused a division of AI into a number of branches. Among this growing list of subcategories, each is often described as separate technologies on account of their own unique characteristics. ...
... Within AI, the progress of the field has caused a division of AI into a number of branches. Among this growing list of subcategories, each is often described as separate technologies on account of their own unique characteristics. ...
Building Knowledge Bases through Multistrategy Learning and
... of the complexity of this problem, the application of Machine Learning tends to be limited to simple domains. While knowledge acquisition research has generally avoided using machine learning techniques, relying on the knowledge engineer, machine learning research has generally avoided involving a h ...
... of the complexity of this problem, the application of Machine Learning tends to be limited to simple domains. While knowledge acquisition research has generally avoided using machine learning techniques, relying on the knowledge engineer, machine learning research has generally avoided involving a h ...
Media Planning and Analysis
... • Media are the general communication methods that carry advertising messages—television, magazines, newspapers, and so on. • Vehicles are the specific broadcast programs or print choices in which advertisements are placed. • For example, television is the media, and American Idol is the vehicle. • ...
... • Media are the general communication methods that carry advertising messages—television, magazines, newspapers, and so on. • Vehicles are the specific broadcast programs or print choices in which advertisements are placed. • For example, television is the media, and American Idol is the vehicle. • ...
Evolving Real-time Heuristic Search Algorithms
... route, regardless of how distant the goal is. Another application of real-time heuristic search is distributed search such as routing in ad hoc sensor networks (Bulitko and Lee, 2006). Starting with LRTA* (Korf, 1990) real-time heuristic search agents interleave three processes: local planning, heur ...
... route, regardless of how distant the goal is. Another application of real-time heuristic search is distributed search such as routing in ad hoc sensor networks (Bulitko and Lee, 2006). Starting with LRTA* (Korf, 1990) real-time heuristic search agents interleave three processes: local planning, heur ...
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.