scholarly journals The Resolution of Gender Anaphora Reference with the Help of Kernel Trick Mechanisms

2019 ◽  
Vol 630 ◽  
pp. 012029
Author(s):  
A A Pak ◽  
S Amirzhan ◽  
A A Ziyaden
Keyword(s):  
Kernel Trick

scholarly journals FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

2021 ◽  
Author(s):  
Hessam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Permutation Test ◽  
Pearson Correlation ◽  
Functional Integration ◽  
Clustering Coefficient ◽  
Kernel Trick ◽  
Non Linear ◽  
The Brain

Abstract Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

scholarly journals A Modified Algorithm for Generalized Discriminant Analysis

2004 ◽  
Vol 16 (6) ◽  
pp. 1283-1297 ◽  
Author(s):  
Wenming Zheng ◽  
Li Zhao ◽  
Cairong Zou
Keyword(s):  
Discriminant Analysis ◽  
Theoretical Analysis ◽  
Face Database ◽  
Kernel Trick ◽  
Linear Discriminant ◽  
Degenerate Eigenvalue ◽  
Discriminant Ability ◽  
Previous Algorithm ◽  
Modified Algorithm ◽  
Generalized Discriminant Analysis

Generalized discriminant analysis (GDA) is an extension of the classical linear discriminant analysis (LDA) from linear domain to a nonlinear domain via the kernel trick. However, in the previous algorithm of GDA, the solutions may suffer from the degenerate eigenvalue problem (i.e., several eigenvectors with the same eigenvalue), which makes them not optimal in terms of the discriminant ability. In this letter, we propose a modified algorithm for GDA (MGDA) to solve this problem. The MGDA method aims to remove the degeneracy of GDA and find the optimal discriminant solutions, which maximize the between-class scatter in the subspace spanned by the degenerate eigenvectors of GDA. Theoretical analysis and experimental results on the ORL face database show that the MGDA method achieves better performance than the GDA method.

scholarly journals PENGEMBANGAN MODEL SUPPORT VECTOR MACHINES (SVM) DENGAN MEMPERBANYAK DATASET UNTUK PREDIKSI BISNIS FOREX MENGGUNAKAN METODE KERNEL TRICK

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Adi Sucipto ◽  
Akhmad Khanif Zyen
Keyword(s):  
Support Vector Machine ◽  
Exchange Rate ◽  
Support Vector Machine Model ◽  
Precious Metals ◽  
Current Data ◽  
Support Vector ◽  
Machine Model ◽  
Kernel Trick ◽  
Currency Exchange ◽  
Currency Exchange Rate

There are many types of investments that can be used to generate income, such as in the form of land, houses, gold, precious metals etc., there are also in the form of financial assets such as stocks, mutual funds, bonds and money markets or capital markets. One of the investments that attract enough attention today is the capital market investment. The purpose of this study is to predict and improve the accuracy of foreign exchange rates on forex business by using the Support Vector Machine model as a model for predicting and using more data sets compared with previous research that is as many as 1558 dataset. This study uses currency exchange rate data obtained from PT. Best Profit Future Cab. Surabaya is already in the form of data consisting of open, high, low, close attributes by using the current data of Euro currency exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 As much as 1689 dataset, After conducting research using Support Vector Machine model with kernel trick method to predict Forex using current data of Euro exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 as much as 1689 The dataset yielded a considerable prediction accuracy of 97.86%, with this considerable accuracy indicating that the movement of the Euro currency exchange rate to the USA Dollar on May 12 to May 13, 2016 can be predicted precisely.

scholarly journals Anomaly Detection using Feature Selection and SVM Kernel Trick

2015 ◽  
Vol 129 (4) ◽  
pp. 31-35
Author(s):  
R. Ravinder ◽  
Y. Ramadevi ◽  
K.V.N Sunitha
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Kernel Trick

scholarly journals Analisis dan Implementasi Metode Gabor Filter dan Support Vector Machine pada Klasifikasi Sidik Jari

2017 ◽  
Vol 2 (2) ◽  
pp. 37
Author(s):  
Intan Raharni Wijaya
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Gabor Filter ◽  
Support Vector ◽  
Kernel Trick ◽  
Radial Basis ◽  
Svm Multiclass

Pengolahan citra digital semakin diminati, salah satunya pada sistem biometrik. Sistem biometrik merupakan sistem dalam pengenalan berdasarkan pola atau ciri khusus yang dimiliki makhluk hidup terutama manusia. Jenis identifikasi biometrik yang umum digunakan adalah pengenalan sidik jari. Sidik jari banyak digunakan dalam kehidupan sehari-hari selama lebih dari 100 tahun karena penerimaan yang tinggi, permanen, akurat, dan keunikan. Kelebihan sidik jari tersebut disebabkan oleh minutiae yang merupakan garis atau guratan pada sidik jari yang berbeda-beda setiap individu. Klasifikasi sidik jari secara umum terbagi menjadi dua tahap yakni ekstraksi fitur serta klasifikasi fitur. <br /> <br /> Ektraksi fitur dapat dilakukan dengan cara filter seperti gabor filter dengan empat sudut orientasi yang berkisar 0, 45, 90 dan 135 derajat. Hasil dari ekstraksi ciri akan klasifikasi dengan tujutan identifikasi. Metode Support Vector Machine (SVM) dapat digunakan sebagai classifier untuk sistem biometrik sidik jari. SVM memiliki kernel trick yang berpengaruh pada akurasi yang dihasilkan. Digunakan SVM multiclass metode one-against-all dalam klasfikasi sidik jari untuk 25 kelas. Akurasi terbesar diperoleh oleh kernel Radial Basis Function (RBF) sebesar 73% untuk data awal dan 76% untuk penambahan data augmentasi

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