Learning-Based Planning
... learning techniques for computing the optimal policy for reaching the given goals by exploring the state space though trial and error. The major benefit of these techniques is that they can be used to solve problems whether the action model is known or not. In the other hand, since RRL does not expl ...
... learning techniques for computing the optimal policy for reaching the given goals by exploring the state space though trial and error. The major benefit of these techniques is that they can be used to solve problems whether the action model is known or not. In the other hand, since RRL does not expl ...
M211 (ITC450 earlier)
... the constraints of the computers resources. Often the goal is to compute a solution as fast as possible, using as few resources as possible. To solve a problem efficiently it may be necessary to use data structures tailored for the particular problem(s) at hand. A data structure is a specific way of ...
... the constraints of the computers resources. Often the goal is to compute a solution as fast as possible, using as few resources as possible. To solve a problem efficiently it may be necessary to use data structures tailored for the particular problem(s) at hand. A data structure is a specific way of ...
Ten Project Proposals in Artificial Intelligence
... until you reach a leaf. The leaf stores the classification (Sunburnt or None). In the present case the decision tree agrees with our intuition about factors that are decisive for getting surnburnt. For example, neither a person’s weight nor height plays a role. It is often possible to construct more ...
... until you reach a leaf. The leaf stores the classification (Sunburnt or None). In the present case the decision tree agrees with our intuition about factors that are decisive for getting surnburnt. For example, neither a person’s weight nor height plays a role. It is often possible to construct more ...
The Foundations of Cost-Sensitive Learning
... training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods. Accordingly, the recommended way of applying one of these methods in a domain with differing misclassification costs is to learn a classifier from the training set as given, and t ...
... training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods. Accordingly, the recommended way of applying one of these methods in a domain with differing misclassification costs is to learn a classifier from the training set as given, and t ...
NNIntro
... What is ANN • Structurally, ANN is a complex, interconnected structure composed of simple processing elements, often mimicking biological neurons • Functionally, ANN is an inductive learning machine, it is able to undergo an adaptation process (learning) driven by examples ...
... What is ANN • Structurally, ANN is a complex, interconnected structure composed of simple processing elements, often mimicking biological neurons • Functionally, ANN is an inductive learning machine, it is able to undergo an adaptation process (learning) driven by examples ...
news summary (44) - Quest Group`s Blog
... hardware to MIT Technology Review last week, and says it plans to launch a product aimed at retailers in the coming months. The long-term ambitions are far grander. Kindred hopes that this human-assisted learning will foster a fundamentally new and more powerful kind of artificial intelligence. Kind ...
... hardware to MIT Technology Review last week, and says it plans to launch a product aimed at retailers in the coming months. The long-term ambitions are far grander. Kindred hopes that this human-assisted learning will foster a fundamentally new and more powerful kind of artificial intelligence. Kind ...
Computational intelligence meets the NetFlix prize IEEE
... Artificial Neural networks consist of a series of layers of nodes, known as artificial neurons, connected by weights. Each node in a layer is connected to every node in the previous layer by a series of weights. The network operates by applying a vector to the input of the network. At each node in t ...
... Artificial Neural networks consist of a series of layers of nodes, known as artificial neurons, connected by weights. Each node in a layer is connected to every node in the previous layer by a series of weights. The network operates by applying a vector to the input of the network. At each node in t ...
XGBoost: A Scalable Tree Boosting System
... the corresponding leaf index. T is the number of leaves in the tree. Each fk corresponds to an independent tree structure q and leaf weights w. Unlike decision trees, each regression tree contains a continuous score on each of the leaf, we use wi to represent score on i-th leaf. For a given example, ...
... the corresponding leaf index. T is the number of leaves in the tree. Each fk corresponds to an independent tree structure q and leaf weights w. Unlike decision trees, each regression tree contains a continuous score on each of the leaf, we use wi to represent score on i-th leaf. For a given example, ...
A conceptual model for game based intelligent tutoring
... Student knowledge acquisition and knowledge retention can be monitored through many different mechanisms. Student modelling can be accomplished using production rules which indicate ideal rules and bad rules (as per rule-based domain models), logic programming using an inference engine to infer reas ...
... Student knowledge acquisition and knowledge retention can be monitored through many different mechanisms. Student modelling can be accomplished using production rules which indicate ideal rules and bad rules (as per rule-based domain models), logic programming using an inference engine to infer reas ...
Artificial Intelligence
... - Schema.org contains millions of RDF triplets describing known facts: search engines can use this data to provide structured information upon request. - The OpenGraph protocol – which uses RDFa – is used by Facebook to enable any web page to become a rich object in a social graph. Finally, another ...
... - Schema.org contains millions of RDF triplets describing known facts: search engines can use this data to provide structured information upon request. - The OpenGraph protocol – which uses RDFa – is used by Facebook to enable any web page to become a rich object in a social graph. Finally, another ...
Fast Imbalanced Classification of Healthcare Data with Missing Values
... prevent creating very small coarse sets for the minority class even if the majority class can still be coarsened. Often, methods for imbalanced classification demonstrate poor performance on data with missing values (such as [16]) that is a frequent situation in healthcare data. Therefore, we apply ...
... prevent creating very small coarse sets for the minority class even if the majority class can still be coarsened. Often, methods for imbalanced classification demonstrate poor performance on data with missing values (such as [16]) that is a frequent situation in healthcare data. Therefore, we apply ...
What`s Hot in Intelligent User Interfaces
... and unable to adapt. The performance metrics used to evaluate these algorithms (e.g., perplexity) are not always consistent with human judgement. To make data analysis more user-friendly, smart interaction techniques such as interactive data visualization are often used. At IUI 2015, we continue to ...
... and unable to adapt. The performance metrics used to evaluate these algorithms (e.g., perplexity) are not always consistent with human judgement. To make data analysis more user-friendly, smart interaction techniques such as interactive data visualization are often used. At IUI 2015, we continue to ...
Differential Roles of the Frontal Cortex, Basal Ganglia, and
... set, though the activity varied with different sets. For the learned hypersets, the neuron fired only at the very beginning: before the first button press for the first set of the first trial. The preference for new sequences was fairly common among neurons in the pre-SMA. A considerable portion of ...
... set, though the activity varied with different sets. For the learned hypersets, the neuron fired only at the very beginning: before the first button press for the first set of the first trial. The preference for new sequences was fairly common among neurons in the pre-SMA. A considerable portion of ...
![[slides] Kernels and clustering](http://s1.studyres.com/store/data/004208335_1-990c8ce2efe99730694ed35e841f416e-300x300.png)
[slides] Kernels and clustering
... If we had a black box (kernel) K that told us the dot product of two examples x and x’: Could work entirely with the dual representation No need to ever take dot products (“kernel trick”) ...
... If we had a black box (kernel) K that told us the dot product of two examples x and x’: Could work entirely with the dual representation No need to ever take dot products (“kernel trick”) ...
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.