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May 2, 2017 - Mp4 (19:44) (162.27 MB). Dr Dobb's Journal-Software Tools for the Professional Programmer 19, 4 (1994), 38--43. Hirofumi Tanaka, Kevin D Monahan, and Douglas R Seals. Spiders in the Sky: User Perceptions of Drones, Privacy, and Security. Download Video Tanaka T22 Hd MP3 3GP MP4 (19:50) - Tonton atau download video Tanaka T22 Hd Desember 2018 di Muvideo.net 100% gratis dan mudah, Down.
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The buyer makes arrangements to have all freight charges billed directly to the buyer. Buyer MUST Arrange Shipping: Due to the nature of the product in the auction, the buyers are required to arrange their own shipping. Buyers arranging their own shipping must contact Buyer Relations and request the shipping charges be removed. Buyers arranging their own shipping must sign a Shipping Waiver Form. Once the transaction is paid, the buyer is sent the necessary forms to sign and return. By signing, the buyer agrees to waive their right to dispute the product.
After the signed forms are returned, the buyer is provided the pick-up location and contact information. Buyers arranging their own shipping are encouraged to inspect the product prior to removing it from the seller's location. The buyer makes arrangements to have all freight charges billed directly to the buyer. Shelf Pulls: Shelf Pulls were previously available for sale in a retail environment but were never sold.
They usually possess one or more price tags and/or stickers, indicating multiple markdowns and have been exposed to appreciable customer contact. In addition, since most of these items are sent through a reverse supply chain (e.g., from a retailer back to a centralized warehouse), they can show signs of further handling. Accordingly, Shelf Pulls can exhibit a wide range of individual product and package conditions that can differ substantially from the original manufacturing.
Used: Used Used Assets were previously sold and put into use. They possess noticeable cosmetic defects and blemishes, including but not limited to dents, scratches, and signs of age. Since these assets are usually pulled from a working environment, they rarely come in original packaging and hardly ever contain any documentation or any additional parts and/or accessories.
They are minimally tested to meet only the most basic requirements of functionality. Used assets therefore may not be in optimal working condition and can require additional maintenance and repair. Returns: Returns were sold to a customer, who then either physically brought the item back to a store or mailed it to a specified location. Reasons for returning a product may not have any correlation to its usefulness (i.e., size, color, model, etc.), and as a result that product may be in fine working order.
The majority of Returns, however, do have some operational and/or cosmetic problem. Depending on a company's return policy, these items may also reflect a measurable amount of use. In addition, since most of these items are sent through a reverse supply chain (e.g., from a customer back to a store or a centralized warehouse), they can show signs of further handling. They generally do not come in original packaging and often do not have any of the advertised documentation or additional parts and/or accessories. Accordingly, Returns can exhibit a wide range of individual product and package conditions that can differ substantially from the original manufacturing.
. Lee, Ching-Pei; Lin, Chih-Jen 2014-04-01 Linear rank SVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rank SVM and gradient boosting decision trees, linear rank SVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rank SVM may give competitive performance for large and sparse data. A great deal of works have studied linear rank SVM. The focus is on the computational efficiency when the number of preference pairs is large.
In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rank SVM tool for public use. Becker, Natalia; Toedt, Grischa; Lichter, Peter; Benner, Axel 2011-05-09 Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine ( SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties.
Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.The penalized SVM. 2011-01-01 Background Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine ( SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed.
Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net. We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone. Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution.
Results Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error. Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. Conclusions The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning.
Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming 2014-11-30 Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods.
Most promising methods are based on support vector machine ( SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (Compare SVM) based on SVM to compare different kernel methods for inference of GRN. Using Compare SVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from Compare SVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (. Kazemian, H B; Yusuf, S A; White, K 2014-02-01 About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways.
Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine ( SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates.
In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM.
For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd. Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu 2017-07-01 In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC- SVM) algorithm (WT-BCC- SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer.
Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC- SVM) and support vector machine ( SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively.
Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC- SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC- SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves. Mei, Suyu; Zhu, Hao 2015-01-26 Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed.
The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions.
Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus. Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua 2013-12-01 The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin.
A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS- SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin.
The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS- SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE-ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS- SVM is superior to that developed by PLS, PCA-LS- SVM or ICA-LS- SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively. Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P 2016-09-01 Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient.
There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions. Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar 2017-01-01 Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS- SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS- SVM (PCA-LS- SVM) model has the highest prediction accuracy with coefficient of determination ( R 2 ) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects.
Furthermore, R 2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R 2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies. Jeon, Jin Pyeong; Kim, Chulho; Oh, Byoung-Doo; Kim, Sun Jeong; Kim, Yu-Seop 2018-01-01 To assess and compare predictive factors for persistent hemodynamic depression (PHD) after carotid artery angioplasty and stenting (CAS) using artificial neural network (ANN) and multiple logistic regression (MLR) or support vector machines ( SVM) models. A retrospective data set of patients (n=76) who underwent CAS from 2007 to 2014 was used as input (training cohort) to a back-propagation ANN using TensorFlow platform. PHD was defined when systolic blood pressure was less than 90mmHg or heart rate was less 50 beats/min that lasted for more than one hour. The resulting ANN was prospectively tested in 33 patients (test cohort) and compared with MLR or SVM models according to accuracy and receiver operating characteristics (ROC) curve analysis.
No significant difference in baseline characteristics between the training cohort and the test cohort was observed. PHD was observed in 21 (27.6%) patients in the training cohort and 10 (30.3%) patients in the test cohort. In the training cohort, the accuracy of ANN for the prediction of PHD was 98.7% and the area under the ROC curve (AUROC) was 0.961. In the test cohort, the number of correctly classified instances was 32 (97.0%) using the ANN model. In contrast, the accuracy rate of MLR or SVM model was both 75.8%. ANN (AUROC: 0.950; 95% CI confidence interval: 0.813-0.996) showed superior predictive performance compared to MLR model (AUROC: 0.796; 95% CI: 0.620-0.915, p.