A Convolution Kernel Method for Color Recognition

2007 ◽  
Author(s):  
Jeong-Woo Son ◽  
Seong-Bae Park ◽  
Ku-Jin Kim
Keyword(s):  
Kernel Method ◽  
Convolution Kernel ◽  
Color Recognition

Kernel Feature Extraction Approach for Color Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1621-1626
Author(s):  
Xiao Yuan Jing ◽  
Kun Li ◽  
Song Song Wu ◽  
Yong Fang Yao ◽  
Chao Wang
Keyword(s):  
Correlation Analysis ◽  
Image Recognition ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Color Image ◽  
Kernel Method ◽  
Recognition Performance ◽  
Image Databases ◽  
Color Recognition ◽  
Kernel Canonical Correlation Analysis

Color Image Recognition is one of the most important fields in Pattern Recognition. Both Multi-set canonical correlation analysis and Kernel method are important techniques in the field of color image recognition. In this paper, we combine the two methods and propose one novel color image recognition approach: color image kernel canonical correlation analysis (CIKCCA). Color image kernel canonical correlation analysis is based on the theory of multi-set canonical correlation analysis and extracts canonical correlation features among the color image components. Then fuse the features of the color image components in the feature level, which are used for classification and recognition. Experimental results on the FRGC-v2 public color image databases demonstrate that the proposed approach acquire better recognition performance than other color recognition methods.

Online Prediction Model Based on New Kernel Method

2014 ◽  
Vol 7 (1) ◽  
pp. 107
Author(s):  
Ilyes Elaissi ◽  
Okba Taouali ◽  
Messaoud Hassani
Keyword(s):  
Prediction Model ◽  
Kernel Method ◽  
Model Based ◽  
Online Prediction

An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method

2009 ◽  
Vol E92-A (10) ◽  
pp. 2514-2521
Author(s):  
Hirosato SEKI ◽  
Fuhito MIZUGUCHI ◽  
Satoshi WATANABE ◽  
Hiroaki ISHII ◽  
Masaharu MIZUMOTO
Keyword(s):  
Fuzzy Inference ◽  
Kernel Method ◽  
Inference Method

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation

2021 ◽  
Author(s):  
Jingjing Tang ◽  
Jiahui Li ◽  
Weiqi Xu ◽  
Yingjie Tian ◽  
Xuchan Ju ◽  
...  
Keyword(s):  
Credit Risk ◽  
Risk Evaluation ◽  
Kernel Method

scholarly journals MLatom 2: An Integrative Platform for Atomistic Machine Learning

2021 ◽  
Vol 379 (4) ◽  
Author(s):  
Pavlo O. Dral ◽  
Fuchun Ge ◽  
Bao-Xin Xue ◽  
Yi-Fan Hou ◽  
Max Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Package ◽  
State Of The Art ◽  
Kernel Method ◽  
Automatic Learning ◽  
Hyperparameter Optimization ◽  
Input And Output ◽  
Spectrum Simulation ◽  
Farthest Point ◽  
Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

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