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[PDF] Top 20 Feature discovery using snap drift neural networks

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Feature discovery using snap drift neural networks

Feature discovery using snap drift neural networks

... using the snap-drift algorithm. It also gives the opportunity to test the performance of SDNN without a performance feedback in a purely unsupervised mode. SDNN categorizes the input patterns ... See full document

11

Updating of Road Network Databases: Spatio Temporal Trajectory Grouping Using Snap Drift Neural Network

Updating of Road Network Databases: Spatio Temporal Trajectory Grouping Using Snap Drift Neural Network

... by snap and drift: snapping gives the angle of the minimum values (on all dimensions) and drifting gives the average angle of the patterns grouped under the ...a feature common to all the patterns in ... See full document

11

Updating Road Network Databases: Road Segment Grouping Using Snap Drift Neural Network

Updating Road Network Databases: Road Segment Grouping Using Snap Drift Neural Network

... of neural networks have been employed in the past for map matching and road extraction ...the Snap-Drift neural network (SDNN) developed by Lee and Palmer-Brown, (2004) is ...and ... See full document

7

Feature selection of microarray data using genetic algorithms and artificial neural networks

Feature selection of microarray data using genetic algorithms and artificial neural networks

... method. Neural networks remain a powerful tool for building ...the discovery of high scoring feature combinations that would have been ignored using a filter method justifies its ... See full document

71

Continuous Reinforced Snap Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

Continuous Reinforced Snap Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

... The outputs produced by the dP-ART act as input to the sP-ART. The behaviour of sP-ART is the same as that described in section P-ART Architecture, with one exception; only the F2 node with the highest activation is ... See full document

12

Diagnostic Feedback by Snap drift Question Response Grouping

Diagnostic Feedback by Snap drift Question Response Grouping

... of networks which perform optimization for classification or equivalents by for example pushing features in the direction that minimizes error, without any requirement for the feature to be statistically ... See full document

8

Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods

Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods

... In spite of the good results for reduction of variance, AR models still have some problems. In Figure 8, the coe\cient of 25 lags for model AR P25Q has been shown. This 9gure shows that by increasing the lags PdelaysQ, ... See full document

8

A Study on Neural Network in Image Processing

A Study on Neural Network in Image Processing

... knowledge discovery, data mining and neuro ...on neural networks. Some aspects of theory of neural networks are addressed such as visualization of processes in neural ... See full document

7

Automated updating of road network databases: road segment grouping using snap drift neural network

Automated updating of road network databases: road segment grouping using snap drift neural network

... than feature discovery by pushing features in the direction that minimizes error on the output nodes without any requirement for the feature to be statistically significant within the input data, ... See full document

9

An exploratory study of GPS trajectory data using Snap Drift Neural Network

An exploratory study of GPS trajectory data using Snap Drift Neural Network

... the feature to be statistically significant within the input ...by snap and drift: snapping gives the angle of the minimum values (on all dimensions) and drifting gives the average angle of the ... See full document

10

Phonetic Feature Discovery in Speech using Snap Drift

Phonetic Feature Discovery in Speech using Snap Drift

... whose weight prototypes best match the current input pattern, are used as the input data to the sSDNN module for feature classification. In both of the modules, the standard matching and reset mechanism of ART  ... See full document

13

Snap Drift Neural Network for Selecting Student Feedback

Snap Drift Neural Network for Selecting Student Feedback

... a feature common to all the patterns in the group and gives a high probability of rapid (in terms of epochs) convergence (both snap and drift are convergent, but snap is ...generalised ... See full document

9

Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks

Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks

... (Hamed et al., 2009), vQEA (Schliebs et al., 2009), Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA) (Schliebs et al., 2010) and new hybrid harmony search algorithm with evolving spiking ... See full document

36

Data Mining using Neural Networks

Data Mining using Neural Networks

... solving a problem through simulated annealing will prove incompatible with that of virtual machines or we can say that while working with virtualization of machines it will be quite incompatible with that of the features ... See full document

6

Face Recognition and Feature Detection Using Artificial Neural Networks and ANFIS

Face Recognition and Feature Detection Using Artificial Neural Networks and ANFIS

... Neuro-fuzzy models, including adaptive neuro fuzzy inference systems (ANFIS) [14], [15] are fuzzy inference systems implemented as neural nets. Each layer in the network correspondsto a part of the FIS: input ... See full document

5

Causal pattern inference from neural spike train data

Causal pattern inference from neural spike train data

... process. Using few or fairly weak assumptions may not be sufficient in order to derive strong statements about the ...assumptions). Using such restricted result for the analysis of biological data requires ... See full document

227

Feature visualization in comic artist classification using deep neural networks

Feature visualization in comic artist classification using deep neural networks

... Deep neural networks, especially convolutional neural networks have achieved a con- siderable success in image analysis [9, 10] and other related applications [11, ...the feature ... See full document

18

Feature based 3D Object Recognition using Artificial Neural Networks

Feature based 3D Object Recognition using Artificial Neural Networks

... The proposed methodology is based on extracting set of features from the 2D images which include the Affine, Zernike and Hu moments invariants to be used as inputs to train artificial ne[r] ... See full document

7

A feature based reverse engineering system using artificial neural networks

A feature based reverse engineering system using artificial neural networks

... 1999 Geometric feature recognition for Reverse Engineering using Neural Networks, International Journal of Advanced Manufacturing Technology submitted and awaiting referee panel's commen[r] ... See full document

300

Classification of Cardiovascular Disease Using Feature Extraction and Artificial Neural Networks

Classification of Cardiovascular Disease Using Feature Extraction and Artificial Neural Networks

... the neural network is presented with a dataset that it has not seen before and produces an ...the neural network to see its predictive power (Figure ... See full document

16

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