SAMPLE SET CONDENSATION FOR A CONDENSED NEAREST NEIGHBOR DECISION RULE FOR PATTERN RECOGNITION

1972 ◽  
pp. 511-526 ◽  
Author(s):  
C.W. Swonger
Keyword(s):  
Pattern Recognition ◽  
Decision Rule ◽  
Nearest Neighbor ◽  
Sample Set

scholarly journals On Some Fuzzy Classification Algorithms and the AEC Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Pattern Recognition ◽  
Decision Rule ◽  
Nearest Neighbor ◽  
Fuzzy Classification ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Recognition Algorithms ◽  
K Nearest Neighbor Algorithm

In the paper some fuzzy classification algorithms based upon a nearest neighbor decision rule areconsidered in terms of the pattern recognition algorithms which are based on the computation of estimates (theso-called AEC model). It is shown that the fuzzy K nearest neighbor algorithm can be assigned to the AECclass. In turn, it is found that some standard AEC algorithms, which depend on a number of numericalparameters, can be used as fuzzy classification algorithms. Yet among them there exist algorithms extremalwith respect to these parameters. Such algorithms provide maximum values of the associated performancemeasures.

scholarly journals Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4566 ◽  
Author(s):  
Zhe Li ◽  
Yongpeng Xu ◽  
Xiuchen Jiang
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Partial Discharge ◽  
Back Propagation ◽  
Training Sample ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Set Size ◽  
Sample Set

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

Graph Embedding Using Dissimilarities with Applications in Classification

2013 ◽  
pp. 363-380
Author(s):  
Horst Bunke ◽  
Kaspar Riesen
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Graph Matching ◽  
Graph Embedding ◽  
Mathematical Structure ◽  
Object Representations ◽  
Mathematical Concepts ◽  
Novel Approach ◽  
Representational Power ◽  
Mathematical Operations

The domain of graphs contains only little mathematical structure. That is, most of the basic mathematical operations, actually required by many standard computer vision and pattern recognition algorithms, are not available for graphs. One of the few mathematical concepts that has been successfully transferred from the vector space to the graph domain is distance computation between graphs, commonly referred to as graph matching. Yet, distance-based pattern recognition is basically limited to nearest-neighbor classification. The present chapter reviews a novel approach for graph embedding in vector spaces built upon the concept of graph matching. The key-idea of the proposed embedding method is to use the distances of an input graph to a number of training graphs, termed prototypes, as vectorial description of the graph. That is, all graph matching procedures proposed in the literature during the last decades can be employed in this embedding framework. The rationale for such a graph embedding is to bridge the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. Hence, the proposed framework can be considered a contribution towards unifying the domains of structural and statistical pattern recognition.

Pattern Synthesis for Nonparametric Pattern Recognition

2011 ◽  
pp. 1511-1516
Author(s):  
P. Viswanath ◽  
Narasimha M. Murty ◽  
Bhatnagar Shalabh
Keyword(s):  
Pattern Recognition ◽  
Density Estimation ◽  
Nearest Neighbor ◽  
Curse Of Dimensionality ◽  
Parametric Methods ◽  
Pattern Synthesis ◽  
Parzen Window ◽  
Pattern Recognition Methods ◽  
The Given ◽  
Non Parametric

Parametric methods first choose the form of the model or hypotheses and estimates the necessary parameters from the given dataset. The form, which is chosen, based on experience or domain knowledge, often, need not be the same thing as that which actually exists (Duda, Hart & Stork, 2000). Further, apart from being highly error-prone, this type of methods shows very poor adaptability for dynamically changing datasets. On the other hand, non-parametric pattern recognition methods are attractive because they do not derive any model, but works with the given dataset directly. These methods are highly adaptive for dynamically changing datasets. Two widely used non-parametric pattern recognition methods are (a) the nearest neighbor based classification and (b) the Parzen-Window based density estimation (Duda, Hart & Stork, 2000). Two major problems in applying the non-parametric methods, especially, with large and high dimensional datasets are (a) the high computational requirements and (b) the curse of dimensionality (Duda, Hart & Stork, 2000). Algorithmic improvements, approximate methods can solve the first problem whereas feature selection (Isabelle Guyon & André Elisseeff, 2003), feature extraction (Terabe, Washio, Motoda, Katai & Sawaragi, 2002) and bootstrapping techniques (Efron, 1979; Hamamoto, Uchimura & Tomita, 1997) can tackle the second problem. We propose a novel and unified solution for these problems by deriving a compact and generalized abstraction of the data. By this term, we mean a compact representation of the given patterns from which one can retrieve not only the original patterns but also some artificial patterns. The compactness of the abstraction reduces the computational requirements, and its generalization reduces the curse of dimensionality effect. Pattern synthesis techniques accompanied with compact representations attempt to derive compact and generalized abstractions of the data. These techniques are applied with (a) the nearest neighbor classifier (NNC) which is a popular non-parametric classifier used in many fields including data mining since its conception in the early fifties (Dasarathy, 2002) and (b) the Parzen-Window based density estimation which is a well known non-parametric density estimation method (Duda, Hart & Stork, 2000).

Myocardial Tissue Characterization Using Pattern Recognition Procedures on Backscattered Ultrasonic Signals

1986 ◽  
Vol 8 (3) ◽  
pp. 181-195
Author(s):  
R.A.G. Dyer ◽  
S.A. Dyer ◽  
P.K. Bhagat
Keyword(s):  
Pattern Recognition ◽  
Minimum Distance ◽  
Tissue Characterization ◽  
Nearest Neighbor ◽  
Myocardial Tissue ◽  
Ultrasonic Signals ◽  
Pattern Recognition Techniques ◽  
Myocardial Tissue Characterization ◽  
Backscattered Signals

Pattern recognition techniques were applied to backscattered signals obtained in vitro from normal and abnormal canine and human heart samples. Orthogonal transforms, in conjunction with the variance criterion, comprised the feature extractors. The minimum-distance (MD) and nearest-neighbor (NN) rules were used as classifiers. When the MD rule was used, the magnitude of the DFT gave the best performance for both canine and human samples. When the NN rule was used, all the transforms performed comparably. The classification performances were improved for both species when the NN rule was used with feature extractors containing phase information.

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