Support vector machines

Hearst, Marti A.; Dumais, Susan T.; Osman, Emad A. M.; Platt, John; Schölkopf, Bernhard

doi:10.1109/5254.708428

Cited by 4,434 publications

(1,639 citation statements)

References 14 publications

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“…Such work could also compare a variety of classification strategies (e.g. random forests [80], support vector machines [81] , k nearest neighbours [82]) and should use appropriate cross validation techniques to estimate system efficacy (accuracy, sensitivity and specificity). The studies which used leave-one-subject-out-cross-validation (LOSOCV) to validate their global movement classification system report that this is the most appropriate cross validation method to estimate the efficacy of the system for a new user who is not included in the classifier's training data [3,23].…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

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Section: Exercise Detection Systemsmentioning

confidence: 99%

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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“…Emoticons can be referred to printable characters of emotion, such as :-) for smile and :-( for sad. SVM [8], [9] with unigram obtained high accuracy at 82.90%. [3] note that using negation and part-of-speech tagging did not help improve accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the content of Tweets and identifying their sentiment polarity as positive or negative is currently an active research topic. There are various researches that use Tweets with machine leaning algorithms; for example, [3] classify Twitter using Naï ve Bayes (NB) [4], [5], Maximum Entropy Modelling [6], [7] and Support Vector Machine (SVM) [8], [9]. In the experiment, emoticons have been used as noisy labels in training data to identify the label as positive or negative.…”

Section: Related Workmentioning

confidence: 99%

Using Arbiter and Combiner Tree to Classify Contexts of Data

Chalothorn¹,

Ellman²

2016

IJCTE

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Abstract-This paper reports on the use of ensemble learning to classify as either positive or negative the sentiment of Tweets. Tweets were chosen as Twitter is a popular tool and a public, human annotated dataset was made available as part of the SemEval 2013 competition. We report on a classification approach that contrasts single machine learning algorithms with a combination of algorithms in an ensemble learning approach. The single machine learning algorithms used were support vector machine (SVM) and Naï ve Bayes (NB), while the methods of ensemble learning include the arbiter tree and the combiner tree. Our system achieved an F-score using Tweets and SMS with the arbiter tree at 83.57% and 93.55%, respectively, which was better than base classifiers; meanwhile, the results from the combiner tree achieved lower scores than base classifiers.

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“…The nonlinear mapping based S 3 VM 26 can map the samples into a high dimensional characteristic space to make them linearly separable. The 27 functional margin that is the distance between the sample and the optimal hyper plane decided by the 28 S 3 VM, can be used as the confidence-based decision marking to make the S 3 VM be effectively 29 exploited by the SSL based methods (Hearst et al 1998; U. Maulik and Chakraborty 2014).…”

Section: Introduction Accuracy To a Certain Extent When Labeling Themmentioning

confidence: 99%

A novel semisupervised support vector machine classifier based on active learning and context information

Gao

Zhang

et al. 2016

Multidim Syst Sign Process

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Support vector machines

Cited by 4,434 publications

References 14 publications

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Using Arbiter and Combiner Tree to Classify Contexts of Data

A novel semisupervised support vector machine classifier based on active learning and context information

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