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0: Rule_Learning: A Neural Network Based Head Tracking System: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to t...
7
cora
train
0: Rule_Learning: A Neural Network Based Head Tracking System: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to t...
7
cora
train
0: Rule_Learning: Meter as Mechanism: A Neural Network that Learns Metrical Patterns: One kind of prosodic structure that apparently underlies both music and some examples of speech production is meter. Yet detailed measurements of the timing of both music and speech show that the nested periodicities that define metri...
7
cora
train
0: Rule_Learning: Fast pruning using principal components. : We present a new algorithm for eliminating excess parameters and improving network generalization after supervised training. The method, "Principal Components Pruning (PCP)", is based on principal component analysis of the node activations of successive laye...
7
cora
train
0: Rule_Learning: Neural networks and statistical models. : 1: Neural_Networks: Learning in design: From Characterizing Dimensions to Working Systems: The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the exp...
7
cora
train
0: Rule_Learning: Avoiding overfitting with BP-SOM. : Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of gen-eralisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, bp-so...
7
cora
train
0: Rule_Learning: An inductive learning approach to prognostic prediction. : This paper introduces the Recurrence Surface Approximation, an inductive learning method based on linear programming that predicts recurrence times using censored training examples, that is, examples in which the available training output may...
7
cora
train
0: Rule_Learning: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations. : SPERT (Synthetic PERceptron Testbed) is a fully programmable single chip microprocessor designed for efficient execution of artificial neural network algorithms. The first implementation will be in a 1.2 m CMOS technology with a...
7
cora
train
0: Rule_Learning: Avoiding overfitting with BP-SOM. : Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of gen-eralisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, bp-so...
7
cora
train
0: Rule_Learning: Predictive Robot Control with Neural Networks: Neural controllers are able to position the hand-held camera of the (3DOF) anthropomorphic OSCAR-robot manipulator above an object which is arbitrary placed on a table. The desired camera-joint mapping is approximated by feedforward neural networks. Howev...
7
cora
train
0: Rule_Learning: MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-te...
7
cora
train
0: Rule_Learning: MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-te...
7
cora
train
0: Rule_Learning: "Active Learning with Statistical Models," : For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select...
7
cora
train
0: Rule_Learning: A simple randomized quantization algorithm for neural network pattern classifiers. : This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomize...
7
cora
train
0: Rule_Learning: "Active Learning with Statistical Models," : For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select...
7
cora
train
0: Rule_Learning: Neural networks and statistical models. : 1: Neural_Networks: Technical Diagnosis: Fallexperte-D of further knowledge sources (domain knowledge, common knowledge) is investigated in the: Case based reasoning (CBR) uses the knowledge from former experiences ("known cases"). Since special knowledge of...
7
cora
train
0: Rule_Learning: MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-te...
7
cora
train
0: Rule_Learning: A Neural Network Based Head Tracking System: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to t...
7
cora
train
0: Rule_Learning: A Neural Network Based Head Tracking System: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to t...
7
cora
train
0: Rule_Learning: MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-te...
7
cora
train
0: Rule_Learning: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations. : SPERT (Synthetic PERceptron Testbed) is a fully programmable single chip microprocessor designed for efficient execution of artificial neural network algorithms. The first implementation will be in a 1.2 m CMOS technology with a...
7
cora
train
0: Rule_Learning: Meter as Mechanism: A Neural Network that Learns Metrical Patterns: One kind of prosodic structure that apparently underlies both music and some examples of speech production is meter. Yet detailed measurements of the timing of both music and speech show that the nested periodicities that define metri...
7
cora
train
0: Rule_Learning: (1995) Constructive Algorithms for Hierachical Mixtures of Experts. : We present two additions to the hierarchical mixture of experts (HME) architecture. We view the HME as a tree structured classifier. Firstly, by applying a likelihood splitting criteria to each expert in the HME we "grow" the tree ...
7
cora
train
0: Rule_Learning: A simple randomized quantization algorithm for neural network pattern classifiers. : This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomize...
7
cora
train
0: Rule_Learning: Pattern analysis and synthesis in attractor neural networks. : The representation of hidden variable models by attractor neural networks is studied. Memories are stored in a dynamical attractor that is a continuous manifold of fixed points, as illustrated by linear and nonlinear networks with hidden ...
7
cora
train
0: Rule_Learning: Predictive Robot Control with Neural Networks: Neural controllers are able to position the hand-held camera of the (3DOF) anthropomorphic OSCAR-robot manipulator above an object which is arbitrary placed on a table. The desired camera-joint mapping is approximated by feedforward neural networks. Howev...
7
cora
train
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