problem stringlengths 2.94k 12.7k | solution int64 7 7 | dataset stringclasses 1
value | split stringclasses 1
value |
|---|---|---|---|
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: 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: 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: 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: 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: 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: 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: 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: 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 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: 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: 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: 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: (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: 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: 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: 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: 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: "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: 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: 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: 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: 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: 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: 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: 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
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 ... | 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: LEARNING FOR DECISION MAKING: The FRD Approach and a Comparative Study Machine Learning and Infe... | 7 | cora | train |
0: Rule_Learning: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
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 an... | 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: 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: 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: 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: 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: 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: 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. Differ... | 7 | cora | train |
0: Rule_Learning: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
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 ... | 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications: This paper proposes a simple cost model for machine learning applications based on the notion of net present value... | 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: 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: "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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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 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: 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: "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: Neural networks and statistical models. :
1: Neural_Networks: "Robot Juggling: An Implementation of Memory-Based Learning," : This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local model ... | 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: 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: 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
1: Neural_Networks: a platform for emergencies management systems. : This paper describe the functional architecture of CHARADE a software platform devoted to the development of a new generation of intelligent env... | 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: 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: 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 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: Spline Smoothing For Bivariate Data With Applications To Association Between Hormones:
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 ... | 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: 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: 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: 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 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: 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.