Issues in evolving GP based classifiers for a pattern recognition task

2004 ◽  
Author(s):  
A.M. Teredesai ◽  
V. Govindaraju
Keyword(s):  
Pattern Recognition ◽  
Recognition Task ◽  
Pattern Recognition Task

The Heartbeat Classification Based on PCA

2012 ◽  
Vol 195-196 ◽  
pp. 402-406
Author(s):  
Xue Qin Chen ◽  
Rui Ping Wang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Task ◽  
Principal Component ◽  
True Positive Rate ◽  
True Positive ◽  
Heartbeat Classification ◽  
Positive Rate ◽  
Electrocardiogram Ecg ◽  
Pattern Recognition Task

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

scholarly journals Electric Source Localization Adds Evidence for Task-Specific CNVs

1998 ◽  
Vol 11 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Ina M. Tarkka ◽  
Luis F. H. Basile
Keyword(s):  
Pattern Recognition ◽  
Source Localization ◽  
Clinical Neurophysiology ◽  
Recognition Task ◽  
Source Analysis ◽  
Visual Spatial ◽  
Spatio Temporal ◽  
Electric Source ◽  
Pattern Recognition Task ◽  
Spatial Recognition

This study was an attempt to replicate recent magnetoencephalographic (MEG) findings on human task-specific CNV sources (Basile et al., Electroencephalography and Clinical Neurophysiology 90, 1994, 157–165) by means of a spatio-temporal electric source localization method (Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 62, 1985, 32–44; Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 65, 1986, 344-360; Scherg and Berg, Brain Electric Source Analysis Handbook, Version 2). The previous MEG results showed CNV sources in the prefrontal cortex of the two hemispheres for two tasks used, namely visual pattern recognition and visual spatial recognition tasks. In the right hemisphere, the sources were more anterior and inferior for the spatial recognition task than for the pattern recognition task. In the present study we obtained CNVs in five subjects during two tasks identical to the MEG study. The elicited electric potentials were modeled with four spatio-temporal dipoles for each task, three of which accounted for the visual evoked response and one that accounted for the CNV. For all subjects the dipole explaining the CNV was always localized in the frontal region of the head, however, the dipole obtained during the visual spatial recognition task was more anterior than the one obtained during the pattern recognition task. Thus, task-specific CNV sources were again observed, although the stable model consisted of only one dipole located close to the midline instead of one dipole in each hemisphere. This was a major difference in the CNV sources between the previous MEG and the present electric source analysis results. We discuss the possible basis for the difference between the two methods used to study slow brain activity that is believed to originate from extended cortical patches.

scholarly journals On the Use of Concentrated Time–Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 911 ◽  
Author(s):  
Sarra Houidi ◽  
Dominique Fourer ◽  
François Auger
Keyword(s):  
Pattern Recognition ◽  
Recognition Task ◽  
Time Frequency ◽  
Neural Architecture ◽  
Signal Representations ◽  
Current State ◽  
Signal Features ◽  
Load Monitoring ◽  
Pattern Recognition Task

Since decades past, time–frequency (TF) analysis has demonstrated its capability to efficiently handle non-stationary multi-component signals which are ubiquitous in a large number of applications. TF analysis us allows to estimate physics-related meaningful parameters (e.g., F0, group delay, etc.) and can provide sparse signal representations when a suitable tuning of the method parameters is used. On another hand, deep learning with Convolutional Neural Networks (CNN) is the current state-of-the-art approach for pattern recognition and allows us to automatically extract relevant signal features despite the fact that the trained models can suffer from a lack of interpretability. Hence, this paper proposes to combine together these two approaches to take benefit of their respective advantages and addresses non-intrusive load monitoring (NILM) which consists of identifying a home electrical appliance (HEA) from its measured energy consumption signal as a “toy” problem. This study investigates the role of the TF representation when synchrosqueezed or not, used as the input of a 2D CNN applied to a pattern recognition task. We also propose a solution for interpreting the information conveyed by the trained CNN through different neural architecture by establishing a link with our previously proposed “handcrafted” interpretable features thanks to the layer-wise relevant propagation (LRP) method. Our experiments on the publicly available PLAID dataset show excellent appliance recognition results (accuracy above 97%) using the suitable TF representation and allow an interpretation of the trained model.

scholarly journals POTENTIAL FUNCTION METHOD APPROACH TO PATTERN RECOGNITION APPLICATIONS

2017 ◽  
Vol 2 ◽  
pp. 30
Author(s):  
Peter Grabusts
Keyword(s):  
Pattern Recognition ◽  
Potential Function ◽  
Function Method ◽  
Recognition Task ◽  
Potential Functions ◽  
Learning Procedure ◽  
Potential Methods ◽  
Pattern Recognition Task ◽  
Method Approach ◽  
Potential Function Method

Potential function method was originally offered to solve the pattern recognition tasks, then it was generalized to a wider range of tasks, which were associated with the function approximation. Potential function method algorithms are based on the hypothesis of the nature of the function that separates sets according to different classes of patterns. Geometrical interpretation of pattern recognition task includes display of patterns in the form of vector in the space of input signal that allows to perceive the learning as approximation task. The paper describes the essence of potential function method and the learning procedure is shown that is based on practical application of potential methods. Pattern recognition applications with the help of examples of potential functions and company bankruptcy data analysis with the help of potential functions are given.

Channel and feature selection for a surface electromyographic pattern recognition task

2014 ◽  
Vol 41 (11) ◽  
pp. 5190-5200 ◽  
Author(s):  
Iker Mesa ◽  
Angel Rubio ◽  
Imanol Tubia ◽  
Joaquin De No ◽  
Javier Diaz
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Recognition Task ◽  
Selection For ◽  
Pattern Recognition Task

scholarly journals CHARACTERIZATION OF THE EFFICIENCY OF THE FEATURES AGGREGATE IN FUZZY PATTERN RECOGNITION TASK

1997 ◽  
Vol 1 ◽  
pp. 78
Author(s):  
R. Grekov ◽  
A. Borisov
Keyword(s):  
Pattern Recognition ◽  
Decision Rule ◽  
Recognition Task ◽  
Dimensional Vector ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern ◽  
The Given ◽  
Pattern Recognition Task

Let a set of objects exist each of which is described by N features X1? ..., XN, where each feature X} is a real number. So each object is set by N-dimensional vector (Xl5 ..., XN) and represents a point in the space of object descriptions, RN.There are also set objects for which degrees of membership in either class are unknown. A decision rule should be determined that could enable estimation of the membership of either object with unknown degrees of membership in the given classes (Ozols and Borisov, 1996). To determine the decision rule, such features should be found which give a possibility to distinguish objects belonging to different classes, i.e. features that are specific for each class. That is why a subtask of estimation of the efficiency of features should be solved. A function 5 should be determined which could enable estimation of the efficiency of both separate features and of features groups.Thus, the task is reduced to the determination of a number of features from set N that will best describe groups of objects and will enable possibly correct recognition of the object's membership in a class.

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