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A Nearest Features Classifier Using a Self-organizing Map for Memory Base Evaluation [chapter]

Christos Pateritsas, Andreas Stafylopatis
2006 Lecture Notes in Computer Science  
Using the self-organizing maps model during the execution of the algorithm, dynamic weights of the memory base patterns are produced.  ...  We propose a new methodology for addressing the classification task that relies on the main idea of the k -nearest neighbors algorithm, which is the most important representative of this field.  ...  This data set of feature combinations forms a new memory base but this memory base is edited with the use of a self-organizing map.  ... 
doi:10.1007/11840930_40 fatcat:za4p25ovdjedde4d3wswr4zum4

Detection of Brain Tumor Using K-Nearest Neighbor (KNN) Based Classification Model and Self Organizing Map (SOM) Algorithm

2020 International journal of neural networks and advanced applications  
The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used.  ...  In this paper a novel approach for the detection of brain tumor is proposed.  ...  Here we use the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used.  ... 
doi:10.46300/91016.2020.7.6 fatcat:otkbn6ym4fhkffijda2ehiyryq

Learning from Web Data with Self-Organizing Memory Module [article]

Yi Tu, Li Niu, Junjie Chen, Dawei Cheng, Liqing Zhang
2020 arXiv   pre-print
ROIs in each bag are assigned with different weights based on the representative/discriminative scores of their nearest clusters, in which the clusters and their scores are obtained via our designed memory  ...  In this paper, we propose a novel method, which is capable of handling these two types of noises together, without the supervision of clean images in the training stage.  ...  For evaluation, we directly use the well-trained CNN model to classify test images based on image-level features without extracting region proposals.  ... 
arXiv:1906.12028v5 fatcat:75qvpao3jfcgljanwdrg5rallm

Color Texture Analysis and Classification: An Agent Approach Based on Partially Self-avoiding Deterministic Walks [chapter]

André Ricardo Backes, Alexandre Souto Martinez, Odemir Martinez Bruno
2010 Lecture Notes in Computer Science  
Here, we have generalized the texture based on the deterministic partially self avoiding walk to analyze and classify colored textures.  ...  This method considers independent walkers, with a given memory, leaving from each pixel of an image.  ...  For instance, consider a partially self-avoiding deterministic walk, where a walker wishes to visit N sites randomly distributed in a map of d dimension.  ... 
doi:10.1007/978-3-642-16687-7_6 fatcat:xpc6xjmvnzgane647z4xfw443y

Learning From Web Data With Self-Organizing Memory Module

Yi Tu, Li Niu, Junjie Chen, Dawei Cheng, Liqing Zhang
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
ROIs in each bag are assigned with different weights based on the representative/discriminative scores of their nearest clusters, in which the clusters and their scores are obtained via our designed memory  ...  In this paper, we propose a novel method, which is capable of handling these two types of noises together, without the supervision of clean images in the training stage.  ...  For evaluation, we directly use the well-trained CNN model to classify test images based on image-level features without extracting region proposals.  ... 
doi:10.1109/cvpr42600.2020.01286 dblp:conf/cvpr/TuNCC020 fatcat:6d5ldesctbhjhfvyl75cmo6kke

Incremental Learning with Self-labeling of Incoming High-dimensional Data

Farzana Anowar, Samira Sadaoui
2021 Proceedings of the Canadian Conference on Artificial Intelligence  
The incremental classifier is then adapted gradually with chunks that are optimally reduced and self-labeled.  ...  Using a highly-dimensional and multi-class dataset, we conduct several experiments to demonstrate our incremental learning approach's efficacy and compare it with incremental learning using human-annotated  ...  Hence, our study combines self-labeling with chunk-based incremental learning, which is essential for classifying and learning efficiently from incoming unlabeled data.  ... 
doi:10.21428/594757db.521714b7 fatcat:27le6utixnasbghdtw6j2si2qa

Effective Pruning Method for a Multiple Classifier System Based on Self-Generating Neural Networks [chapter]

Hirotaka Inoue, Hiroyuki Narihisa
2003 Lecture Notes in Computer Science  
Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning.  ...  Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy.  ...  SGNN are an extension of the self-organizing maps (SOM) of Kohonen [7] and utilize the competitive learning which is implemented as a self-generating neural tree (SGNT).  ... 
doi:10.1007/3-540-44989-2_2 fatcat:jqlpjsnmijcnzkzlt7cag7kx5a

EXPLORING FEATURES AND CLASSIFIERS TO CLASSIFY GENE EXPRESSION PROFILES OF ACUTE LEUKEMIA

SUNG-BAE CHO
2002 International journal of pattern recognition and artificial intelligence  
Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification.  ...  In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers.  ...  with respect to these free parameters. 2 Self-organizing map Self-organizing map (SOM) defines a mapping from the input space onto an output layer by unsupervised learning algorithm.  ... 
doi:10.1142/s0218001402002015 fatcat:d3gljkjvcvcehcbsnjrpmxl2hm

Self-organizing Neural Grove [chapter]

Hirotaka Inoue
2014 Lecture Notes in Computer Science  
Self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of their simple setting and fast learning.  ...  In this paper, we propose a novel pruning method for effective processing and we call this model as self-organizing neural grove (SONG).  ...  SGNT are an extension of the self-organizing maps (SOM) of Kohonen [7] and utilize the competitive learning. The abilities of SGNT make it suitable for the base classifier of the MCS.  ... 
doi:10.1007/978-3-319-12637-1_18 fatcat:hk5s6ifwxbd2lec3xgquxjifva

Isolated Handwritten Pashto Character Recognition Using a K-NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

Juanjuan Huang, Ihtisham Ul Haq, Chaolan Dai, Sulaiman Khan, Shah Nazir, Muhammad Imtiaz, Dr Shahzad Sarfraz
2021 Complexity  
K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets.  ...  A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation.  ...  using a k-NN classifier based on the accumulated feature map using HoG and zoning techniques.  ... 
doi:10.1155/2021/5558373 fatcat:xlronet2ybhlrjruqgv4c25fny

A Survey on Triangle Area Map Based Multivariate Correlation Analysis to Detect Denial-Of Service Attack
IJARCCE - Computer and Communication Engineering

K. SUJITHRA, V.VINOTH KUMAR
2014 IJARCCE  
Additionally Triangle Area Based Technique is used to speed up the process of Multivariate Correlation Analysis (MCA). Proposed system can be evaluated by using KDD cup dataset  ...  This system includes anomaly detection method for the detection of known and unknown Dos.  ...  Emergent Self Organizing Map It classify "normal" traffic against "abnormal" traffic in the sense of Dos attack.  ... 
doi:10.17148/ijarcce.2014.31028 fatcat:mjmubr54tbfijnnzmsdjbsdixa

Anomaly Detection via Self-organizing Map [article]

Ning Li, Kaitao Jiang, Zhiheng Ma, Xing Wei, Xiaopeng Hong, Yihong Gong
2021 arXiv   pre-print
Our method, Self-organizing Map for Anomaly Detection (SOMAD) maintains normal characteristics by using topological memory based on multi-scale features.  ...  In this paper, we propose a novel unsupervised anomaly detection approach based on Self-organizing Map (SOM).  ...  To alleviate the aforementioned problems, we proposed a novel and efficient approach: self-organizing map for anomaly detection (SOMAD).  ... 
arXiv:2107.09903v1 fatcat:ka7w3sfgzzdujayu4he4svivn4

DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed with Deep Features

Ayan Sinha, Chiho Choi, Karthik Ramani
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We efficiently identify 'spatial' nearest neighbors to the activation feature, from a database of features corresponding to synthetic depth maps, and store some 'temporal' neighbors from previous frames  ...  We discriminatively train convolutional neural networks to output a low dimensional activation feature given a depth map.  ...  Related Work Approaches for hand-pose estimation can be broadly classified as either generative (model-based) or discriminative (appearance based) methods.  ... 
doi:10.1109/cvpr.2016.450 dblp:conf/cvpr/SinhaCR16 fatcat:ysuzkkimbrh5ba5v4p3gmevuwe

Global Peer-to-Peer Classification in Mobile Ad-Hoc Networks: A Requirements Analysis [chapter]

Dawud Gordon, Markus Scholz, Yong Ding, Michael Beigl
2011 Lecture Notes in Computer Science  
Using these requirements, related work is evaluated for applicability, indicating no adequate solutions.  ...  Algorithmic approaches are proposed, and analysis results in a benchmark as well as bounds for distribution of processing load, memory consumption and message passing in P2P-MANETs.  ...  usage and communication volumes were elaborated, and a brute force (upper bound) and neural network (close to lower bound) approach were examined.  ... 
doi:10.1007/978-3-642-24279-3_12 fatcat:quqqs6p6pfgxjocodxdkayjzam

Multiclass classification of distributed memory parallel computations

Sean Whalen, Sean Peisert, Matt Bishop
2013 Pattern Recognition Letters  
We first provide relevant background on message passing and computational equivalence classes called dwarfs and describe our exploratory data analysis using Self Organizing Maps.  ...  As a first step towards addressing this, we introduce a machine learning approach for classifying distributed parallel computations based on communication patterns between compute nodes.  ...  Self-Organizing Maps To explore relationships between features we perform unsupervised clustering using a type of neural net called a Self-Organizing Map (SOM) [14] .  ... 
doi:10.1016/j.patrec.2012.10.007 fatcat:y3oe4zylrnhojkve6fwinbnflq
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