On the Learnability of Description Logic Programs
... restrictions, which allow to quantify the amount of indeterminism of these relations. However, up to now there are neither practical nor theoretical results concerning learning the Carin-ALN language. This paper is intended to close this gap, by showing how Carin-ALN learning can be embedded into f ...
... restrictions, which allow to quantify the amount of indeterminism of these relations. However, up to now there are neither practical nor theoretical results concerning learning the Carin-ALN language. This paper is intended to close this gap, by showing how Carin-ALN learning can be embedded into f ...
Improving the scalability of rule
... compared our representation against the representation for continuous attributes of the NAX system (Llorà et al, 2008), integrated inside BioHEL. We have decided to compare our representation against this one because it was also explicitly designed for efficiency purposes, using SSE vectorial instr ...
... compared our representation against the representation for continuous attributes of the NAX system (Llorà et al, 2008), integrated inside BioHEL. We have decided to compare our representation against this one because it was also explicitly designed for efficiency purposes, using SSE vectorial instr ...
Unsupervised Many-to-Many Object Matching for Relational Data
... Object matching is the task of finding correspondences between objects in different domains. Examples of object matching include document alignment [1] and sentence alignment [2], [3] in natural language processing, matching images and annotations in computer vision [4], and matching user identifier ...
... Object matching is the task of finding correspondences between objects in different domains. Examples of object matching include document alignment [1] and sentence alignment [2], [3] in natural language processing, matching images and annotations in computer vision [4], and matching user identifier ...
A Partitioned Fuzzy ARTMAP Implementation for Fast Processing of
... the algorithm and lends itself well to parallel implementation. Under the fairly reasonable assumption that the number of templates in the ART neural network is proportional to the number of training patterns we can state that the ART convergence time is quadratic O(n2 ) where n is the number of pa ...
... the algorithm and lends itself well to parallel implementation. Under the fairly reasonable assumption that the number of templates in the ART neural network is proportional to the number of training patterns we can state that the ART convergence time is quadratic O(n2 ) where n is the number of pa ...
Training neural networks II
... • Can we learn representations that are robust to loss of neurons? Intuition: learn and remember useful information even if there are some errors in the computation (biological connection?) ...
... • Can we learn representations that are robust to loss of neurons? Intuition: learn and remember useful information even if there are some errors in the computation (biological connection?) ...
A Stochastic Algorithm for Feature Selection in Pattern Recognition
... Blanket Filtering has been likewise competitive in feature selection for video application in Liu and Render (2003). The second approach (wrapper methods) is computationally demanding, but often is more accurate. A wrapper algorithm explores the space of features subsets to optimize the induction al ...
... Blanket Filtering has been likewise competitive in feature selection for video application in Liu and Render (2003). The second approach (wrapper methods) is computationally demanding, but often is more accurate. A wrapper algorithm explores the space of features subsets to optimize the induction al ...
Learning Abductive Reasoning Using Random Examples
... rather than 1/δ (but in general we might be satisfied with the latter). Furthermore, we could consider an “improper” version of the problem, finding representations from some larger, possibly more expressive class than the H containing the “optimal” hypothesis h∗ . The form of the representation is ...
... rather than 1/δ (but in general we might be satisfied with the latter). Furthermore, we could consider an “improper” version of the problem, finding representations from some larger, possibly more expressive class than the H containing the “optimal” hypothesis h∗ . The form of the representation is ...
Intrusion Detection using Fuzzy Clustering and Artificial Neural
... clustering and neural networks for an Intrusion Detection System (IDS). While neural networks are effective in capturing the non-linearity in data provided, it also has certain limitations including the requirement of high computational resources. By clustering the data, each ANN is trained on a par ...
... clustering and neural networks for an Intrusion Detection System (IDS). While neural networks are effective in capturing the non-linearity in data provided, it also has certain limitations including the requirement of high computational resources. By clustering the data, each ANN is trained on a par ...
Learning to Solve Complex Planning Problems
... learning: when you find a problem that you cannot solve, practice solving auxiliary problems until you see how to solve the original problem. Rather than doing all the practice problems ahead of time, you can learn only what you need to know. People who use lazy-evaluation learning may not learn the ...
... learning: when you find a problem that you cannot solve, practice solving auxiliary problems until you see how to solve the original problem. Rather than doing all the practice problems ahead of time, you can learn only what you need to know. People who use lazy-evaluation learning may not learn the ...
Reports on the 2012 AAAI Fall Symposium Series
... computers can emulate them. Another distinguishing dichotomy dividing the two fields is the use of statistical or machine-learning methods in detecting jokes within computational humor, competing there with meaning, knowledge, and rule-based approaches, of which two, again, AI would be more interest ...
... computers can emulate them. Another distinguishing dichotomy dividing the two fields is the use of statistical or machine-learning methods in detecting jokes within computational humor, competing there with meaning, knowledge, and rule-based approaches, of which two, again, AI would be more interest ...
Computational Creativity
... collaborator rather than a mere tool. Historically, it’s been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. Even within computer science, people are still sceptical about the creative potential of sof ...
... collaborator rather than a mere tool. Historically, it’s been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. Even within computer science, people are still sceptical about the creative potential of sof ...
KEEL Data-Mining Software Tool: Data Set Repository, Integration of
... • Classification problems. This category includes all the supervised data sets. All these data sets contains one or more attributes which label the instances, mapping them into different classes. We distinguish four subcategories of classification data sets: – Standard data sets. – Imbalanced data s ...
... • Classification problems. This category includes all the supervised data sets. All these data sets contains one or more attributes which label the instances, mapping them into different classes. We distinguish four subcategories of classification data sets: – Standard data sets. – Imbalanced data s ...
Prediction of lower extremities` movement by angle–angle diagrams
... identify the defects in the movement and for applying the cyclograms in the control system of the actuators of prosthesis or rehabilitation facilities. This is why we use artificial intelligence methods which are implemented in MatLab toolboxes [18], [19]. We can use, for example, the artificial neu ...
... identify the defects in the movement and for applying the cyclograms in the control system of the actuators of prosthesis or rehabilitation facilities. This is why we use artificial intelligence methods which are implemented in MatLab toolboxes [18], [19]. We can use, for example, the artificial neu ...
CS6659-ARTIFICIAL INTELLIGENCE
... example, at a road junction, the taxi can turn left, turn right, or go straight on. The correct decision depends on where the taxi is trying to get to. In other words, as well as a current state description, the agent needs some sort of goal information that describes situations that are desirable-f ...
... example, at a road junction, the taxi can turn left, turn right, or go straight on. The correct decision depends on where the taxi is trying to get to. In other words, as well as a current state description, the agent needs some sort of goal information that describes situations that are desirable-f ...
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.