The Proximal Trajectory Algorithm in SVM Cross Validation

Astorino, Annabella; Fuduli, Antonio

doi:10.1109/tnnls.2015.2430935

Cited by 28 publications

(9 citation statements)

References 27 publications

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“…[7]), an approach exhibiting both good generalisation capabilities and high computational efficiency due to only requiring the solution of a convex problem for the training phase. The latter characteristic allows to experiment with several different variants of the basic model in order to adapt it to different setting, see for example the recent works [2,3,6,9]. The main idea in the SVM technique is the introduction of the concept of "margin" in the strict separation of two sets of points by means of a hyperplane.…”

Section: The Modelmentioning

confidence: 99%

Ellipsoidal classification via semidefinite programming

Astorino

Frangioni

Gorgone

et al. 2023

Operations Research Letters

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Section: The Modelmentioning

confidence: 99%

Ellipsoidal classification via semidefinite programming

Astorino

Frangioni

Gorgone

et al. 2023

Operations Research Letters

Self Cite

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“…In the table, only the cross-validation accuracies are mentioned. A training-test sample proportion of 30% and 10-fold cross validation was used in all cases [ 51 ]. For all models, grid search [ 52 ] with cross-validation was used to determine the optimal hyperparameters.…”

Section: Identification Of Error Patterns In Eye Gaze Datamentioning

confidence: 99%

MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Kar

2020

Vision

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Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In previous research on pattern analysis of gaze data, efforts were made to model human visual behaviors and cognitive processes. What remains relatively unexplored are questions related to identifying gaze error sources as well as quantifying and modeling their impacts on the data quality of eye trackers. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.

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“…Based on an assumption that environmental elements around Acer mono trees would have impacts on Acer mono sap exudation based on farmers' experiences of increasing Acer mono sap exudation according to big daily temperature range due to the osmotic pressure effects and drying and decreasing Acer mono sap exudation according to increased temperature, the present study designed an Acer mono sap exudation prediction model with a total of seven parameters: average temperature, high, low, daily temperature range, maximum humidity, minimum humidity, and precipitation according to four algorithms, i.e., Linear regression [26][27][28][29][30][31][32][33][34][35], SVM [36][37][38][39][40][41][42][43][44][45][46], ANN [47][48][49][50][51][52][53][54][55][56], and Random forest [57][58][59][60][61]. Linear regression predicts and classifies based on linear regression equations derived from the analysis of correlations between dependent and independent variables.…”

Section: Evaluation Of Acer Mono Sap Output Amount Prediction Modelmentioning

confidence: 99%

“…SVM was embodied with scikit-learn. Since Acer mono sap exudation was predicted in high dimensions with seven parameters, the RBF kernel that was efficient even in high dimensions was used to search for an optimal model [36][37][38][39][40][41][42][43][44][45][46].…”

Section: Support Vector Machine Modelmentioning

confidence: 99%

A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model

et al. 2020

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This study set out to invent an Information and Communication Technologies (ICT)-based smart Acer mono sap collection electric device to make efficient use of the labor force by reducing inefficient activities of old manual work to record sap exudation and state information. Based on the assumption that environmental information would have close connections with Acer mono sap exudation to reinforce the competitive edge of production in forest products, the study analyzed correlations between Acer mono sap exudation and environmental information and predicted Acer mono exudation. A smart collection of electric devices would gather data about Acer mono sap exudation per hour on outdoor temperature, humidity, conductivity, and wind direction and velocity, and was installed in four areas in the Republic of Korea, including Sancheong, Gwangyang, Geoje, and Inje. Collected data were used to analyze correlations between environmental information and Acer mono sap exudation using four different algorithms, including linear regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and random forest, to predict Acer mono sap exudation. Remarkable outcomes were obtained across all the algorithms except for linear regression, demonstrating close connections between environmental information and Acer mono sap exudation. The random forest model, which showed the most outstanding performance, was used to make a mobile app capable of providing predicted Acer mono sap exudation and collected environmental information.

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The Proximal Trajectory Algorithm in SVM Cross Validation

Cited by 28 publications

References 27 publications

Ellipsoidal classification via semidefinite programming

Ellipsoidal classification via semidefinite programming

MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model

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