problem stringlengths 2.94k 12.7k | solution int64 7 7 | dataset stringclasses 1
value | split stringclasses 1
value |
|---|---|---|---|
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: 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: Problem Solving for Redesign: A knowledge-level analysis of complex tasks like diagnosis and design can give us a better understanding of these tasks in terms of the goals they aim to achieve and the different ways to achieve these goals.... | 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: (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: 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: 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: 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: (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: 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: 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: Neural networks and statistical models. :
1: Neural_Networks: What DaimlerBenz has learned as an industrial partner from the machine learning project Statlog. Working Notes for Applying Machine Learning in Practice: : Author of this paper was co-ordinator of the Machine Learning project StatLog dur... | 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: Neural networks and statistical models. :
1: Neural_Networks: Adapting Abstract Knowledge: For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the exte... | 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: Neural networks and statistical models. :
1: Neural_Networks: D.B. Leake. Modeling Case-based Planning for Repairing Reasoning Failures. : One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures of its own reasoning process. We address th... | 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: 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: 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 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: 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: 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: 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: Inverse Entailment and Progol. : This paper firstly provides a re-appraisal of the development o... | 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: 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: 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: 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: (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: Neural networks and statistical models. :
1: Neural_Networks: Task-oriented Knowledge Acquisition and Reasoning for Design Support Systems. : We present a framework for task-driven knowledge acquisition in the development of design support systems. Different types of knowledge that enter the knowle... | 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 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: 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: 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: Opportunistic Reasoning: A Design Perspective. : An essential component of opportunistic behavior is opportunity recognition, the recognition of those conditions that facilitate the pursuit of ... | 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: 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 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: (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: 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: 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: 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: 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: (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: 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: 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: 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: 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: 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: 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 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: 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: 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: 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: 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: 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: 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: 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: 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: Neural networks and statistical models. :
1: Neural_Networks: A Model-Based Approach to Blame Assignment in Design. : We analyze the blame-assignment task in the context of experience-based design and redesign of physical devices. We identify three types of blame-assignment tasks that differ in the... | 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: 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: Neural networks and statistical models. :
1: Neural_Networks: Opportunistic Reasoning: A Design Perspective. : An essential component of opportunistic behavior is opportunity recognition, the recognition of those conditions that facilitate the pursuit of some suspended goal. Opportunity recognition... | 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: 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: 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: 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: (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: 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: On the Greediness of Feature Selection Algorithms: Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not s... | 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: 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: "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: 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: 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: 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: 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: 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: D.B. Leake. Modeling Case-based Planning for Repairing Reasoning Failures. : One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures... | 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: 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: 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: 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: 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: 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: 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: 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.