Medical Diagnosis with C4.5 Rule Preceded by Artificial
... showed that the generalization ability of learning systems based on artificial neural networks can be significantly improved through ensembling artificial neural networks, i.e. training multiple artificial neural networks and combining their predictions. Subsequently there appears a hot wave in inve ...
... showed that the generalization ability of learning systems based on artificial neural networks can be significantly improved through ensembling artificial neural networks, i.e. training multiple artificial neural networks and combining their predictions. Subsequently there appears a hot wave in inve ...
Classifier Ensembles for Detecting Concept Change in Streaming
... are deemed non-significant and are perceived as noise. The classifier should not respond to minor fluctuations, and can use the noisy data to improve its robustness for the underlying stationary distribution. The second plot (Blip) represents a ‘rare event’. Rare events can be regarded as outliers i ...
... are deemed non-significant and are perceived as noise. The classifier should not respond to minor fluctuations, and can use the noisy data to improve its robustness for the underlying stationary distribution. The second plot (Blip) represents a ‘rare event’. Rare events can be regarded as outliers i ...
On the Difficulty of Modular Reinforcement Learning for Real-World Partial Programming
... this assumption may be reasonable for toy problems, we argue that this is not the case for real-world, multiple-goal problems. In practice, it is rare that a “ground truth” reward signal available; instead, we as problem solvers are the ones designing the reward signal. The reward signal design proc ...
... this assumption may be reasonable for toy problems, we argue that this is not the case for real-world, multiple-goal problems. In practice, it is rare that a “ground truth” reward signal available; instead, we as problem solvers are the ones designing the reward signal. The reward signal design proc ...
TagSpace: Semantic Embeddings from Hashtags
... and H was set to 1000. After the convolutional step, a tanh nonlinearity followed by a max operation over the l × H features extracts a fixedsize (H-dimensional) global feature vector, which is independent of document size. Finally, another tanh non-linearity followed by a fully connected linear lay ...
... and H was set to 1000. After the convolutional step, a tanh nonlinearity followed by a max operation over the l × H features extracts a fixedsize (H-dimensional) global feature vector, which is independent of document size. Finally, another tanh non-linearity followed by a fully connected linear lay ...
Abstract:
... Although there is a large variety in the methodology of stylometry, the techniques may be roughly divided into two classes: statistical methods and automated pattern recognition methods. The statistical group of methods normally features the application of Bayes’ Rule in various ways to predict the ...
... Although there is a large variety in the methodology of stylometry, the techniques may be roughly divided into two classes: statistical methods and automated pattern recognition methods. The statistical group of methods normally features the application of Bayes’ Rule in various ways to predict the ...
knowledge base
... inference processes consist of a chain of steps that can be traced by the expert system. This enables expert systems to explain their reasoning processes, which is an important and positive characteristic of expert systems. You would expect a doctor to explain how he or she came to a certain diagnos ...
... inference processes consist of a chain of steps that can be traced by the expert system. This enables expert systems to explain their reasoning processes, which is an important and positive characteristic of expert systems. You would expect a doctor to explain how he or she came to a certain diagnos ...
Retrieval of the diffuse attenuation coefficient Kd(λ)
... Way to improve the estimation • Use of artificial neural networks Æ MultiLayer Perceptron (MLP) ...
... Way to improve the estimation • Use of artificial neural networks Æ MultiLayer Perceptron (MLP) ...
On the Sample Complexity of Reinforcement Learning with a Generative Model
... the best existing lower bound of RL by an order of 1/(1 − γ). The new results, which close the above-mentioned gap between the lower bound and the upper bound, guarantee that no learning method, given the generative model of the MDP, can be significantly more efficient than QVI in terms of the sampl ...
... the best existing lower bound of RL by an order of 1/(1 − γ). The new results, which close the above-mentioned gap between the lower bound and the upper bound, guarantee that no learning method, given the generative model of the MDP, can be significantly more efficient than QVI in terms of the sampl ...
Curriculum Vitae
... at the Computing Laboratory, in Oxford. I believe these are still being taught there. ...
... at the Computing Laboratory, in Oxford. I believe these are still being taught there. ...
learning motor skills by imitation: a biologically inspired robotic model
... movements. The algorithm was trained by comparing the desired motion (as observed during the demonstration) to that achieved through numerous trials by the robot. The model presented in this article intends to bring three new contributions with respect to other models of imitation: First, the model ...
... movements. The algorithm was trained by comparing the desired motion (as observed during the demonstration) to that achieved through numerous trials by the robot. The model presented in this article intends to bring three new contributions with respect to other models of imitation: First, the model ...
Designing and Building an Analytics Library with the Convergence
... • DV-9 categorizes our Biomolecular simulation application with data produced by an HPC simulation • DV-10 is Geospatial Information Systems covered by our spatial algorithms. • DV-7 provenance, is an example of an important feature that we are not covering. • The data storage and access DV-3 and D- ...
... • DV-9 categorizes our Biomolecular simulation application with data produced by an HPC simulation • DV-10 is Geospatial Information Systems covered by our spatial algorithms. • DV-7 provenance, is an example of an important feature that we are not covering. • The data storage and access DV-3 and D- ...
MTH-4153-2
... Representing physical spaces and organizing a layout often call for varied and personal approaches, which could lead adult learners to express their ideas and use their intuition. For example, they could determine how they will go about drawing a scale diagram of a room or a piece of land. Since obj ...
... Representing physical spaces and organizing a layout often call for varied and personal approaches, which could lead adult learners to express their ideas and use their intuition. For example, they could determine how they will go about drawing a scale diagram of a room or a piece of land. Since obj ...
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.