problem stringlengths 2.94k 12.7k | solution int64 7 7 | dataset stringclasses 1
value | split stringclasses 1
value |
|---|---|---|---|
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 |
0: Rule_Learning: Parametrization studies for the SAM and HMMER methods of hidden Markov model generation. : Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generatio... | 7 | cora | train |
0: Rule_Learning: Brain-Structured Networks That Perceive and Learn. : This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for the need for, and usefulness of, appropriate successively larger brain-like structures; and examines parallel-hierarchical Recognition Cone models ... | 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: Parametrization studies for the SAM and HMMER methods of hidden Markov model generation. : Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generatio... | 7 | cora | train |
0: Rule_Learning: Neural networks and statistical models. :
1: Neural_Networks: A Case-based Approach to Reactive Control for Autonomous Robots. : We propose a case-based method of selecting behavior sets as an addition to traditional reactive robotic control systems. The new system (ACBARR | A Case BAsed Reactive R... | 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: Bayesian training of backpropagation networks by the hybrid monte carlo method. : It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of tr... | 7 | cora | train |
0: Rule_Learning: Statistical mechanics of nonlinear nonequilibrium financial markets: Applications to optimized trading, : A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multi... | 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: 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: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 7 | cora | train |
0: Rule_Learning: Bayesian training of backpropagation networks by the hybrid monte carlo method. : It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of tr... | 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: 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: GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES: Wahba, Wang, Gu, Klein and Klein (1995) introduced Smoothing Spline ANalysis of VAriance (SS ANOVA) method for data from exponential families. Based on RKPACK, which fits SS ANOVA models to Gaussian data, we introduce GRKPACK: 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: "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: 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: Bayesian training of backpropagation networks by the hybrid monte carlo method. : It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of tr... | 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: (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: 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: "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: 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: Brain-Structured Networks That Perceive and Learn. : This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for the need for, and usefulness of, appropriate successively larger brain-like structures; and examines parallel-hierarchical Recognition Cone models ... | 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
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 promis... | 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: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 7 | cora | train |
0: Rule_Learning: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 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: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 7 | cora | train |
0: Rule_Learning: GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES: Wahba, Wang, Gu, Klein and Klein (1995) introduced Smoothing Spline ANalysis of VAriance (SS ANOVA) method for data from exponential families. Based on RKPACK, which fits SS ANOVA models to Gaussian data, we introduce GRKPACK: a ... | 7 | cora | train |
0: Rule_Learning: Statistical mechanics of nonlinear nonequilibrium financial markets: Applications to optimized trading, : A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multi... | 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: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 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: (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 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: 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: 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: 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: 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: 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: 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: Dynamically adjusting concepts to accommodate changing contexts. : In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to desc... | 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: Learning High Utility Rules by Incorporating Search Control Guidance Committee:
2: Case_Based: d d Code Scheduling for Multiple Instruction Stream Architectures: Extensive research has been do... | 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: GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES: Wahba, Wang, Gu, Klein and Klein (1995) introduced Smoothing Spline ANalysis of VAriance (SS ANOVA) method for data from exponential families. Based on RKPACK, which fits SS ANOVA models to Gaussian data, we introduce GRKPACK: a ... | 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: (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: 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: Bayesian training of backpropagation networks by the hybrid monte carlo method. : It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of tr... | 7 | cora | train |
0: Rule_Learning: Parametrization studies for the SAM and HMMER methods of hidden Markov model generation. : Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generatio... | 7 | cora | train |
0: Rule_Learning: Parametrization studies for the SAM and HMMER methods of hidden Markov model generation. : Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generatio... | 7 | cora | train |
0: Rule_Learning: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 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: (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 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: GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES: Wahba, Wang, Gu, Klein and Klein (1995) introduced Smoothing Spline ANalysis of VAriance (SS ANOVA) method for data from exponential families. Based on RKPACK, which fits SS ANOVA models to Gaussian data, we introduce GRKPACK: a ... | 7 | cora | train |
0: Rule_Learning: PREENS Tutorial How to use tools and NN simulations: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description.
1: Neural... | 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: 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: Parametrization studies for the SAM and HMMER methods of hidden Markov model generation. : Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generatio... | 7 | cora | train |
0: Rule_Learning: Statistical mechanics of nonlinear nonequilibrium financial markets: Applications to optimized trading, : A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multi... | 7 | cora | train |
0: Rule_Learning: Bayesian training of backpropagation networks by the hybrid monte carlo method. : It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of tr... | 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: 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: 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: 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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