In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines

Anguita, Davide; Ghio, Alessandro; Oneto, Luca; Ridella, Sandro

doi:10.1109/tnnls.2012.2202401

Cited by 103 publications

(90 citation statements)

References 48 publications

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“…In order to tune, in a data dependent manner, the different hyperparameters of the RF of Algorithm 1 and to estimate the performance of the final model, the nonparametric Bootstrap (BOO) is exploited [2]. BOO relies on a simple idea: the original dataset D n is resampled once or many (n o ) times with replacement, to build three independent datasets called training, validation, and test sets, respectively…”

Section: Proposed Data Driven Approachmentioning

confidence: 99%

“…RF are usually preferred to other classification techniques, because of their high numerical robustness, their innate capacity of dealing with numerical and categorical features, and their effectiveness in many real-world problems [10,24]. By carefully tuning the RF hyperparameters [22] and by assessing the performance of the final learned model with state-of-the-art resampling techniques [2], authors will show the effectiveness of the proposal.…”

Section: Introductionmentioning

confidence: 99%

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Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction

Oneto

Coraddu

Sanetti

et al. 2017

Artificial Neural Networks and Machine Learning – ICANN 2017

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Abstract.Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal.

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Section: Proposed Data Driven Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction

Oneto

Coraddu

Sanetti

et al. 2017

Artificial Neural Networks and Machine Learning – ICANN 2017

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“…In the SLT and Structural Risk Minimization (SRM) frameworks [Vapnik, 1995], a good generalization capability on previously unseen data can be guaranteed [Anguita et al, 2012a;Vapnik, 1995] if a nested structure of the available hypothesis sets with increasing complexity is defined (H 1 ⊆ H 2 ⊆ . .…”

Section: Hf-svm and Statistical Learning Theorymentioning

confidence: 99%

“…In the last few years, proposed data dependent bounds [Bartlett and Mendelson, 2003;Bartlett et al, 2005] are becoming tighter and providing better interpretation of the generalization ability of classifiers. They have shown to work well on the performance estimation of real world problems such as in [Anguita et al, 2012a]. For these reason, the understanding of the influence of fixed-point arithmetic approaches in the estimation of these bounds is an interesting topic of research.…”

Section: Hf-svm and Statistical Learning Theorymentioning

confidence: 99%

Energy-Efficient Technologies in Device-to-Device Based Proximity Service

Wang¹,

Vasilakos²,

Jin³

et al. 2017

Device-to-Device Based Proximity Service

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ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author The first stage to accomplish HAR requires to make observations from ambient or wearable sensor technologies. However, in the second case, the search for pervasive, unobtrusive, low-powered, and low-cost devices for achieving this challenging task still has not been fully addressed. In this thesis, we explore the use of smartphones as an alternative approach for performing the identification of physical activities. These self-contained devices, which are widely available in the market, are provided with embedded sensors, powerful computing capabilities and wireless communication technologies that make them highly suitable for this application.This work presents a series of contributions regarding the development of HAR systems with smartphones. In the first place we propose a fully operational system that recognizes in real...

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“…One reason is that it is difficult to determine the VC dimension [32]. A simple and commonly used way to obtain these parameters is the trial-and-error method despite some proposals, for instance analytic parameter selection directly from the training data [33], in-sample and out-of-sample method [34], dynamic particle filter [35], etc. To improve the performance of SVM, this paper makes use of PSO to determine SVM parameters.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Control for Ship Course-Keeping Using Optimized Support Vector Machines

Luo

Cong

2016

Algorithms

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Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.

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In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines

Cited by 103 publications

References 48 publications

Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction

Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction

Energy-Efficient Technologies in Device-to-Device Based Proximity Service

Control for Ship Course-Keeping Using Optimized Support Vector Machines

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