SVM and ANN: A comparative evaluation

Sahay, Tanvi; Aggarwal, Arpit; Bansal, Annu; Chandra, Mahesh

doi:10.1109/ngct.2015.7375263

Cited by 7 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the ANNs often overfit if training goes on too long, so for a given pattern, an ANN might start to consider the noise as part of the pattern [56,57]. SVMs do not suffer from either of these problems [16].…”

Section: Discussionmentioning

confidence: 99%

“…Among the various machine learning approaches, ANNs, Bayesian networks (BN) and support vector machines (SVMs) are the most frequently applied techniques [15,16]. These approaches are algorithms with significant importance for processes where the application of conventional models is complicated by the complexity and unknown conditions affecting the process or by changing environmental conditions.…”

Section: Data-driven Models For the Simulation Of Hydrological Processesmentioning

confidence: 99%

See 1 more Smart Citation

Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series

Langhammer

Česák

2016

Water

View full text Add to dashboard Cite

This paper analyzes the potential of a nu-support vector regression (nu-SVR) model for the reconstruction of missing data of hydrological time series from a sensor network. Sensor networks are currently experiencing rapid growth of applications in experimental research and monitoring and provide an opportunity to study the dynamics of hydrological processes in previously ungauged or remote areas. Due to physical vulnerability or limited maintenance, networks are prone to data outages, which can devaluate the unique data sources. This paper analyzes the potential of a nu-SVR model to simulate water levels in a network of sensors in four nested experimental catchments in a mid-latitude montane environment. The model was applied to a range of typical runoff situations, including a single event storm, multi-peak flood event, snowmelt, rain on snow and a low flow period. The simulations based on daily values proved the high efficiency of the nu-SVR modeling approach to simulate the hydrological processes in a network of monitoring stations. The model proved its ability to reliably reconstruct and simulate typical runoff situations, including complex events, such as rain on snow or flooding from recurrent regional rain. The worst model performance was observed at low flow periods and for single peak flows, especially in the high-altitude catchments.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Data-driven Models For the Simulation Of Hydrological Processesmentioning

confidence: 99%

Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series

Langhammer

Česák

2016

Water

View full text Add to dashboard Cite

show abstract

“…Because data points in ETT with drift were linearly inseparable, ACCESS generated a training model by utilizing the labeled ETT (Fig. 4(c)) as training data through an optimized neural network (NN) algorithm [46] instead of SVM. The training model was applied to this ETT for reclassification.…”

Section: Identification Of Transition States In Etts Using Ac-cessmentioning

confidence: 99%

Combination of density-clustering and supervised classification for event identification in single-molecule force spectroscopy data

et al. 2023

View full text Add to dashboard Cite

Single-molecule force spectroscopy (SMFS) measurements of the dynamics of biomolecules typically require identifying massive events and states from large data sets, such as extracting rupture forces from force-extension curves (FECs) in pulling experiments and identifying states from extension-time trajectories (ETTs) in force-clamp experiments. The former is often accomplished manually and hence is time-consuming and laborious while the latter is always impeded by the presence of base-line drift. In this study, we attempt to accurately and automatically identify the events and states from SMFS experiments with a machine learning approach, ACCESS (approach combining clustering and classification for event identification of SMFS). As demonstrated by analysis of a series of data sets, ACCESS can extract the rupture forces from FECs containing multiple unfolding steps and classify the rupture forces into the corresponding conformational transitions. Moreover, ACCESS successfully identifies the unfolded and folded states even though the ETTs display severe non-monotonic base-line drift. Besides, ACCESS is straightforward in use as it requires only three easy-to-interpret parameters. As such, we anticipate that ACCESS will be a useful, easy-to-implement and high-performance tool for event and state identification across a range of single-molecule experiments.

show abstract

“…Whereas the distance-based weighting takes the inverse of the distances between the input sample and its k nearest neighbors into account, the uniform assigns the same weight for each neighbor as the name suggests. C-SVC is a type of support vector machine (SVM) that can employ, for example, linear, polynomial, radial basis function, and sigmoid kernels [32]. Fitting an SVC is based on structural risk minimization and aims to find a hyperplane that maximizes the distance between the hyperplane and the data points of different classes that are closest to it [33].…”

Section: ) Other Classifiersmentioning

confidence: 99%

“…Fitting an SVC is based on structural risk minimization and aims to find a hyperplane that maximizes the distance between the hyperplane and the data points of different classes that are closest to it [33]. A kernel is a function that is used to compute how similar two input samples are [32].…”

Section: ) Other Classifiersmentioning

confidence: 99%

ATSC-NEX: Automated Time Series Classification With Sequential Model-Based Optimization and Nested Cross-Validation

Tahkola

Zou

2022

IEEE Access

View full text Add to dashboard Cite

New methods to perform time series classification arise frequently and multiple state-of-theart approaches achieve high performance on benchmark datasets with respect to accuracy and computation time. However, often the modeling procedures do not include proper validation but rather rely only on either external test dataset or one-level cross-validation. ATSC-NEX is an automated procedure that employs sequential model-based optimization together with nested cross-validation to build an accurate and properly validated time series classification model. It aims to find an optimal pipeline configuration that includes the selection of input type and settings, as well as model type and hyperparameters. The results of a case study in which a model for the identification of diesel engine type is developed, show that the algorithm can efficiently find a well-performing pipeline configuration. The comparison between ATSC-NEX and some state-of-the-art methods on several benchmark datasets shows that similar accuracy can be achieved.

show abstract

SVM and ANN: A comparative evaluation

Cited by 7 publications

References 9 publications

Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series

Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series

Combination of density-clustering and supervised classification for event identification in single-molecule force spectroscopy data

ATSC-NEX: Automated Time Series Classification With Sequential Model-Based Optimization and Nested Cross-Validation

Contact Info

Product

Resources

About