Methods of Artificial Intelligence – Fuzzy Logic
... adjusting shapes and positions of membership functions within a universal set. Membership functions may represent a subjective record of fuzzy pictures ( low voltage for example). They can be defined according to statistic data, however they are not arbitrarily associated, they are based on the crit ...
... adjusting shapes and positions of membership functions within a universal set. Membership functions may represent a subjective record of fuzzy pictures ( low voltage for example). They can be defined according to statistic data, however they are not arbitrarily associated, they are based on the crit ...
Combining satisfiability techniques from AI and OR
... The obvious solution is to have a look at their pieces and see if there is anything that you can use. This is a fair metaphor for the current relationship between the fields of artificial intelligence (AI) and operations research (OR). The development of successful methods to solve constraint satisf ...
... The obvious solution is to have a look at their pieces and see if there is anything that you can use. This is a fair metaphor for the current relationship between the fields of artificial intelligence (AI) and operations research (OR). The development of successful methods to solve constraint satisf ...
Health Monitoring for Elderly: An Application Using Case
... This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their simil ...
... This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their simil ...
Deep neural networks - Cambridge Neuroscience
... activates according to a nonlinear function. We will refer to model “neurons” as units, in order to maintain a distinction between the biological reality and the highly abstracted models. The perhaps simplest model unit is a linear unit, which outputs a linear combination of its inputs (Fig. 1a). Su ...
... activates according to a nonlinear function. We will refer to model “neurons” as units, in order to maintain a distinction between the biological reality and the highly abstracted models. The perhaps simplest model unit is a linear unit, which outputs a linear combination of its inputs (Fig. 1a). Su ...
Noise Tolerant Data Mining
... exceptions in the learning model. These methods focus more on optimizing the structure of the model, rather than diagnosing possible erroneous data entries. Consequently, they may not take much effect on learning from the data that contains erroneous entries, especially when the errors in the source ...
... exceptions in the learning model. These methods focus more on optimizing the structure of the model, rather than diagnosing possible erroneous data entries. Consequently, they may not take much effect on learning from the data that contains erroneous entries, especially when the errors in the source ...
Hybrid cryptography using symmetric key encryption
... the majority networks to acquire the required data. Because of the defect of only the single data encryption and the use of famous encryption algorithm, which was not improved in traditional methods of the registration process, a combined encryption algorithm is proposed in this thesis[1- 4]. This p ...
... the majority networks to acquire the required data. Because of the defect of only the single data encryption and the use of famous encryption algorithm, which was not improved in traditional methods of the registration process, a combined encryption algorithm is proposed in this thesis[1- 4]. This p ...
A Restricted Markov Tree Model for Inference and
... of adjacent swaps required to transform R into µ). Several recent papers discuss efficient inference over mixtures of Mallows models (or classes which include them) via Gibbs sampling [12, 17]. An alternative, simple, preference model is the random utility model or RUM [14, 18]. In this model, the s ...
... of adjacent swaps required to transform R into µ). Several recent papers discuss efficient inference over mixtures of Mallows models (or classes which include them) via Gibbs sampling [12, 17]. An alternative, simple, preference model is the random utility model or RUM [14, 18]. In this model, the s ...
Learning-Based Planning
... order to prove their performance improvement. Additionally, these systems are not exhaustively evaluated; typically the evaluation only focuses on a very small number of domains, so these planners are usually quite fragile when encountering new domains. Therefore, the community needs a formal method ...
... order to prove their performance improvement. Additionally, these systems are not exhaustively evaluated; typically the evaluation only focuses on a very small number of domains, so these planners are usually quite fragile when encountering new domains. Therefore, the community needs a formal method ...
Free PDF
... This paper presents an approach for computer network traffic characterization by using Time Series Analysis and Computational Intelligence techniques. HTTP network traffic datasets grouped into different periods of day were analyzed under Kurtosis, DFA and SOM-based clustering algorithms. The result ...
... This paper presents an approach for computer network traffic characterization by using Time Series Analysis and Computational Intelligence techniques. HTTP network traffic datasets grouped into different periods of day were analyzed under Kurtosis, DFA and SOM-based clustering algorithms. The result ...
Dr. Eick`s Introduction to AI
... • Heuristo (greek): I find • Copes with problems for which it is not feasible to look at all solutions • Heuristics: rules a thumb (help you to explore the more promising solutions first), based on experience, frequently fuzzy • Main ideas of heuristics: search space reduction, ordering solutions in ...
... • Heuristo (greek): I find • Copes with problems for which it is not feasible to look at all solutions • Heuristics: rules a thumb (help you to explore the more promising solutions first), based on experience, frequently fuzzy • Main ideas of heuristics: search space reduction, ordering solutions in ...
part_3
... Puzzle (cont’d.) • Backtracking algorithm – Find problem solutions by constructing partial solutions – Ensures partial solution does not violate requirements – Extends partial solution toward completion – If partial solution does not lead to a solution (dead end) • Algorithm backs up • Removes most ...
... Puzzle (cont’d.) • Backtracking algorithm – Find problem solutions by constructing partial solutions – Ensures partial solution does not violate requirements – Extends partial solution toward completion – If partial solution does not lead to a solution (dead end) • Algorithm backs up • Removes most ...