scholarly journals Robust Procedures for Estimating and Testing in the Framework of Divergence Measures

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 430
Author(s):  
Leandro Pardo ◽  
Nirian Martín
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Pattern ◽  
Divergence Measures ◽  
Robust Procedures

The approach for estimating and testing based on divergence measures has become, in the last 30 years, a very popular technique not only in the field of statistics, but also in other areas, such as machine learning, pattern recognition, etc [...]

Reaping the benefits of machine learning pattern recognition in nanophotonic component design

2019 ◽  
Author(s):  
Yuri Grinberg ◽  
Daniele Melati ◽  
Mohsen Kamandar Dezfouli ◽  
Siegfried Janz ◽  
Jens Schmid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Pattern ◽  
Component Design

Design of multi-parameter photonic devices using machine learning pattern recognition

2020 ◽  
Author(s):  
Daniele Melati ◽  
Yuri Grinberg ◽  
Mohsen Kamandar Dezfouli ◽  
Jens H. Schmid ◽  
Pavel Cheben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Photonic Devices ◽  
Learning Pattern

Neuroimaging of bipolar disorder

2020 ◽  
pp. 744-756
Author(s):  
Mary L Phillips ◽  
Wayne C Drevets
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Bipolar Disorder ◽  
Pattern Recognition ◽  
Psychiatric Disorders ◽  
Multimodal Neuroimaging ◽  
Protein Marker ◽  
Pattern Recognition Techniques ◽  
Learning Pattern ◽  
Youth At Risk

This chapter discusses findings from recent major neuroimaging studies of bipolar disorder to provide a better understanding of larger-scale neural circuitry, neurotransmitter concentration, bioenergetic process, and protein marker abnormalities in the disorder. The chapter also reviews findings from newer areas of neuroimaging research, including studies comparing bipolar disorder with other major psychiatric disorders, multimodal neuroimaging studies, studies of youth with, and youth at risk for, the disorder, and studies using machine-learning pattern recognition techniques. These studies are paving the way for identification of robust and objective neural biomarkers of bipolar disorder that can ultimately have clinical utility.

Machine learning pattern recognition in integrated silicon photonics design

2020 ◽  
Author(s):  
Dan-Xia Xu ◽  
Daniele Melati ◽  
Mohsen Kamandar Dezfouli ◽  
Jens H. Schmid ◽  
Pavel Cheben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Silicon Photonics ◽  
Learning Pattern ◽  
Integrated Silicon Photonics

scholarly journals SUPPORT VECTOR MACHINES UNTUK MENYELESAIKAN MASALAH KLASIFIKASI PADA PENGENALAN POLA

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Abdul Azis Abdillah
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machines ◽  
Support Vector ◽  
Classification Problems ◽  
Learning Methods ◽  
Learning Pattern ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Learning Machine

ABSTRACTSupport Vector Machines (SVM) are known as the latest machine learning (machine learning) methods to solve classification problems in pattern recognition. This paper discusses the use of SVM in solving problems in pattern recognition. An example of the problem given in this paper contains a collection of data on Any Linearly Separable Datase, Any dataset with Noise, and Real datasets.Key words: machine learning, pattern recognition, SVMABSTRAKSupport Vector Machines (SVM) dikenal sebagai metode machine learning (pembelajaran mesin) paling mutakhir untuk menyelesaikan masalah klasifikasi pada pengenalan pola. Tulisan ini bertujuan untuk membahas penggunaan SVM dalam memecahkan masalah klasifikasi pada pengenalan pola. Contoh masalah yang diberikan pada tulisan ini meliputi klasifikasi data pada Sembarang Linearly Separable Dataset, Sembarang Dataset dengan Noise, dan Real dataset.Kata kunci : klasifikasi, pengenalan pola, SVM

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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