Human-Robot-Communication and Machine Learning
... to develop techniques which allow untrained users to make ecient and safe use of a robot. Two basic aspects characterize the interaction between the robot system and the user (Fig. 1, (Dillmann et al., 1995)). Firstly, the user wants to con gure and, if necessary, instruct the robot for the task at ...
... to develop techniques which allow untrained users to make ecient and safe use of a robot. Two basic aspects characterize the interaction between the robot system and the user (Fig. 1, (Dillmann et al., 1995)). Firstly, the user wants to con gure and, if necessary, instruct the robot for the task at ...
Automatically Building Special Purpose Search Engines with
... • Emission probabilities within each state ei(b) = P( xi = b | i = k) ei(b1) + … + ei(bM) = 1, for all states i = 1…K ...
... • Emission probabilities within each state ei(b) = P( xi = b | i = k) ei(b1) + … + ei(bM) = 1, for all states i = 1…K ...
Search Engines - cloudfront.net
... • K-means and K-nearest neighbor clustering require us to choose K, the number of clusters • No theoretically appealing way of choosing K • Depends on the application and data • Can use hierarchical clustering and choose the best level of the hierarchy to use • Can use adaptive K for K-nearest neigh ...
... • K-means and K-nearest neighbor clustering require us to choose K, the number of clusters • No theoretically appealing way of choosing K • Depends on the application and data • Can use hierarchical clustering and choose the best level of the hierarchy to use • Can use adaptive K for K-nearest neigh ...
Sensitivity to sampling in Bayesian word learning
... not predict this contrast, because both types of trials presented the learner with three, practically identical word–object pairings. If anything, there was more variance among the objects in the three-example condition, so it is hard to see why a simple associative learner would generalize more con ...
... not predict this contrast, because both types of trials presented the learner with three, practically identical word–object pairings. If anything, there was more variance among the objects in the three-example condition, so it is hard to see why a simple associative learner would generalize more con ...
The CLARION Cognitive Architecture: A Tutorial
... •" CLARION can learn even when no a priori or externally provided explicit knowledge is available! •" However, it can make use of it when such knowledge is available ! •" Externally provided knowledge, in the forms of explicit conceptual structures (such as rules, plans, categories, and so on), can ...
... •" CLARION can learn even when no a priori or externally provided explicit knowledge is available! •" However, it can make use of it when such knowledge is available ! •" Externally provided knowledge, in the forms of explicit conceptual structures (such as rules, plans, categories, and so on), can ...
Universal Artificial Intelligence: Practical Agents and Fundamental
... Abstract Foundational theories have contributed greatly to scientific progress in many fields. Examples include Zermelo-Fraenkel set theory in mathematics, and universal Turing machines in computer science. Universal Artificial Intelligence (UAI) is an increasingly well-studied foundational theory f ...
... Abstract Foundational theories have contributed greatly to scientific progress in many fields. Examples include Zermelo-Fraenkel set theory in mathematics, and universal Turing machines in computer science. Universal Artificial Intelligence (UAI) is an increasingly well-studied foundational theory f ...
review - NYU Psychology
... with the paired stimuli. Observational learning may also be subserved by social inference, in which the conspecific’s fear expression is a CS that was previously associated with a directly experienced aversive event (US) and may act as a secondary reinforcer in future learning. The study of fear lea ...
... with the paired stimuli. Observational learning may also be subserved by social inference, in which the conspecific’s fear expression is a CS that was previously associated with a directly experienced aversive event (US) and may act as a secondary reinforcer in future learning. The study of fear lea ...
PDF - Bentham Open
... Department of Biological Sciences, University of North Texas, Denton, Texas 76203, USA Abstract: A theoretical model for deriving the origin of emotional functions from first principles is introduced. The model, called “Emotional Model Of the Theoretical Interpretations Of Neuroprocessing”, abbrevia ...
... Department of Biological Sciences, University of North Texas, Denton, Texas 76203, USA Abstract: A theoretical model for deriving the origin of emotional functions from first principles is introduced. The model, called “Emotional Model Of the Theoretical Interpretations Of Neuroprocessing”, abbrevia ...
course-file-soft-computing
... The weights represent information being used by the net to solve problem. 5. Write the logistic sigmoid function? f(x) = 1/(1+exp(-x)). 6. What is the important characteristics that artificial neural network share with biological neural system? Fault tolerance. 7. Name some application of artificial ...
... The weights represent information being used by the net to solve problem. 5. Write the logistic sigmoid function? f(x) = 1/(1+exp(-x)). 6. What is the important characteristics that artificial neural network share with biological neural system? Fault tolerance. 7. Name some application of artificial ...
over deliver
... question with a, “because, I think gaining control of the centre area a useful strategy at this point in the game” question. Whereas. a Deep Learning system cannot. It is not rule-based and cannot easily track its “reasoning”. In a sense it is like an experienced financial markets trader who knows w ...
... question with a, “because, I think gaining control of the centre area a useful strategy at this point in the game” question. Whereas. a Deep Learning system cannot. It is not rule-based and cannot easily track its “reasoning”. In a sense it is like an experienced financial markets trader who knows w ...
MTH_4173-2
... The goal of the Geometric Representation in a Fundamental Context 1 course is to enable adult learners to use trigonometry to deal with situations that involve the geometric representation of an object or a physical space in a fundamental context. In this course, adult learners encounter various sit ...
... The goal of the Geometric Representation in a Fundamental Context 1 course is to enable adult learners to use trigonometry to deal with situations that involve the geometric representation of an object or a physical space in a fundamental context. In this course, adult learners encounter various sit ...
PowerPoint 프레젠테이션 - University at Buffalo
... - AAAI 98 (American Association for Artificial Intelligence ...
... - AAAI 98 (American Association for Artificial Intelligence ...
Computational Intelligence in Data Mining
... models can be evaluated evaluated along the dimensions of predictive accuracy, novelty, utility, and understandability of the fitted model. Traditionally, algorithms to obtain classifiers have focused either on accuracy or interpretability. Recently some approaches to combining these properties have ...
... models can be evaluated evaluated along the dimensions of predictive accuracy, novelty, utility, and understandability of the fitted model. Traditionally, algorithms to obtain classifiers have focused either on accuracy or interpretability. Recently some approaches to combining these properties have ...
Data Averaging and Data Snooping
... of the mean and standard deviation of the results from multiple trials) and data snooping in the context of neural networks, one of the most popular AI machine learning models. Both of these processes can result in misleading results and inaccurate conclusions. We demonstrate how easily this can hap ...
... of the mean and standard deviation of the results from multiple trials) and data snooping in the context of neural networks, one of the most popular AI machine learning models. Both of these processes can result in misleading results and inaccurate conclusions. We demonstrate how easily this can hap ...
Generative Adversarial Structured Networks
... The generative adversarial learning paradigm has significantly advanced the field of unsupervised learning. The adversarial framework pits a generator against a discriminator in a non-cooperative two-player game: the generator’s goal is to generate artificial samples that are convincing enough to be ...
... The generative adversarial learning paradigm has significantly advanced the field of unsupervised learning. The adversarial framework pits a generator against a discriminator in a non-cooperative two-player game: the generator’s goal is to generate artificial samples that are convincing enough to be ...
The Non-Action-Centered
... reasoning, while ACT-R has to use cumbersome pair-wise similarity relations. CLARION has a general functional approximation capability (in its bottom level), while ACT-R does not. ...
... reasoning, while ACT-R has to use cumbersome pair-wise similarity relations. CLARION has a general functional approximation capability (in its bottom level), while ACT-R does not. ...
Learning From Massive Noisy Labeled Data for Image
... tance, which has quadratic complexity with the number of samples thus cannot be applied on large-scale datasets. Weston et al. [27] proposed to embed a pairwise loss in the middle layer of a deep neural network, which benefits the learning of discriminative features. But they needed extra informati ...
... tance, which has quadratic complexity with the number of samples thus cannot be applied on large-scale datasets. Weston et al. [27] proposed to embed a pairwise loss in the middle layer of a deep neural network, which benefits the learning of discriminative features. But they needed extra informati ...
complete file
... usually stored in a distributed fashion. A main objective of mapping subsymbolic information into a symbolic representation is to find an abstract representation of the symbol or object, which is invariant with respect to various features (e.g. invariant with respect to position and orientation). M ...
... usually stored in a distributed fashion. A main objective of mapping subsymbolic information into a symbolic representation is to find an abstract representation of the symbol or object, which is invariant with respect to various features (e.g. invariant with respect to position and orientation). M ...
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.