FOREX Trend Classification using Machine Learning Techniques

Feature extraction Machine-learning techniques Bagging Trees SVM Forex prediction 1 Introduction This paper is about predicting the Foreign Exchange (Forex) market trend using classification and machine learning techniques for the sake of gaining long-term profits Our trading strategy is to take one action per day where this action is either buy or sell based on the prediction we have

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Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification Understanding Support Vector Machines Separable Data Nonseparable Data Nonlinear Transformation with Kernels Separable Data You can use a support vector machine (SVM) when your data has exactly two classes An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of the other class The

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Frontiers

SVM-RFE is an iterative procedure in which unimportant features are removed based on their SVM weights Compared to the simple filters SVM-RFE takes dependencies among features into account Here we used the greedy approach to SVM-RFE (56 66) The effects of FS on an exemplary feature set are illustrated in Figure 1

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Hidden Markov modelling of contourlet transforms for art

MATLAB Central Community Home MATLAB Answers File Exchange Cody Blogs Distance Learning Community SimBiology Community Power Electronics Community Highlights Advisors Virtual badges About Files Authors My File Exchange Contribute About Trial software You are now following this Submission You will see updates in your activity feed You may receive emails depending on your

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1 Introduction

SVM based recursive feature elimination (SVM-RFE) was used for eliminating features Principal component analysis (PCA) was applied for data uncorrelation Results Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51 19% and 64 37% and mean specificities of 72 71% and 39 90% for LDA and SVM respectively Using a Gaussian kernel PCA resulted in

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Open source machine

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors

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Svm Rfe Matlab Code Pdf

Svm rfe matlab code List of ebooks and manuels about Svm rfe matlab code Optimal Design of a Compliant Rear Mountain Bike Suspension doc: Download 558report doc - We have designed and built a custom Matlab code to analyze this space and minimize a multi-objective Matlab Optimization Code

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machine learning

libsvm "reaching max number of iterations" warning and cross-validation Ask Question Asked 7 years 8 months ago Active 1 year 3 months ago Viewed 22k times 21 4 $begingroup$ I'm using libsvm in C-SVC mode with a polynomial kernel of degree 2 and I'm required to train multiple SVMs Each training set has 10 features and 5000 vectors During training I am getting this warning for

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European Journal of Scientific Research

European Journal of Scientific Research is a peer-reviewed scientific research journal that addresses both applied and theoretical issues The scope of the journal encompasses research articles original research reports reviews short communications and scientific commentaries in the fields of applied and theoretical sciences biology chemistry physics zoology medical studies

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Support Vector Machine Classification of Protein

5-5-2020In this study protein sequences are assigned to functional families using machine learning techniques The assignment is based on support vector machine classification of binary feature vectors denoting the presence or absence in the protein of highly conserved sequences of amino-acids called motifs Since the input vectors of the classifier consist of a great number of motifs feature

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MC in Mammogram Using Selected Geometry and

The AUCs for the F-score Relief SVM-RFE SVM-RFE (mRMR) and SRN methods are 0 9014 0 8916 0 9121 0 9236 and 0 9439 respectively with a tenfold cross-validation procedure and are 0 9312 0 9178 0 9324 0 9413 and 0 9615 respectively with a LOO scheme Both the accuracy and AUC values show that the proposed SRN feature selection method has the best performance In addition to

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Final Gleason Score Prediction Using Discriminant Analysis

Objective This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters Materials and Methods Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled

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Support Vector Machine Classification of Protein

In this study protein sequences are assigned to functional families using machine learning techniques The assignment is based on support vector machine classification of binary feature vectors denoting the presence or absence in the protein of highly conserved sequences of amino-acids called motifs Since the input vectors of the classifier consist of a great number of motifs feature

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Research Article Binary Matrix Shuffling Filter for

Research Article Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification CongweiSun 1 2 ZhijunDai 1 2 HongyanZhang 1 2 3 LanzhiLi 1 2 andZhemingYuan 1 2 Hunan Provincial Key Laboratory of Crop Germplasm Innovation and

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Imaging Biomarker Analysis of Structural MR Images for

SVM-RFE top-ranked features for training and testing the linear SVM classifier with leave-one-out cross validation (LOOCV) achieving: 95% Accuracy 95% Sensitivity 93 9% Specificity and 94 4% Area Under Curve Conclusion: The pilot evaluation of the proposed method highlights the value of the advanced qualitative analysis of MRI data in identifying Imaging Biomarkers for the global non

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Regularization and Variable Selection via the Elastic Net

Elastic Net regularization SVM RFE 2/38 1/34 31 PLR RFE 2/38 1/34 26 NSC 2/38 2/34 21 Elastic Net 2/38 0/34 45 UR: univariate ranking (Golub et al 1999) RFE: recursive feature elimination (Guyon et al 2002) SVM: support vector machine (Guyon et al 2002) PLR: penalized logistic regression (Zhu and Hastie 2004) NSC: nearest shrunken centroids (Tibshirani et al 2002) ElasticNet Hui Zou

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SVMRFE Svmrfe after feature selection to sort SVM

matlab File Size: 1KB Update: 2012-03-19 Downloads: 0 Uploaded by: aiaiyouchou Description: Svmrfe after feature selection to sort SVM data mining machine learning Downloaders recently: [More information of uploader aiaiyouchou]] To Search: File list (Click to check if it's the file you need and recomment it at the bottom): SVMRFE m Main Category SourceCode/Document E-Books Document

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Computer

In Traditional Chinese Medicine (TCM) the lip diagnosis is an important diagnostic method which has a long history and is applied widely The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body However the traditional diagnostic approach is mainly based on observation by doctor's nude eyes which is non-quantitative and subjective

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