Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile

Bosch, Paul; López, Julio; Ramírez, Héctor; Robotham, Hugo

doi:10.1016/j.eswa.2013.01.006

Cited by 30 publications

(10 citation statements)

References 17 publications

(19 reference statements)

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“…Similar to [ 54 ], a nonlinear version can be also derived for ξ -SOCP via the kernel trick. The kernel-based ξ -SOCP method is as follows:

where K = [ K 11 , K 12 ; K 21 , K 22 ] ∈ ℜ m × m , with K 11 = AA T , K 12 = K 21 T = BA T , K 22 = BB T and

…”

Section: Materials and Methods: Support Vector Machines And Featurmentioning

confidence: 99%

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

Bosch

Herrera

López

et al. 2018

Behavioural Neurology

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We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.

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“…Similar to [ 54 ], a nonlinear version can be also derived for ξ -SOCP via the kernel trick. The kernel-based ξ -SOCP method is as follows:

where K = [ K 11 , K 12 ; K 21 , K 22 ] ∈ ℜ m × m , with K 11 = AA T , K 12 = K 21 T = BA T , K 22 = BB T and

…”

Section: Materials and Methods: Support Vector Machines And Featurmentioning

confidence: 99%

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

Bosch

Herrera

López

et al. 2018

Behavioural Neurology

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, in [18], three species of fish were identified using acoustic echo sounder surveys and applying the SVM classifier. Sonar echoes were parametrized by a set of features: morphology, acoustical energy, bathymetry and school-shore distance.…”

Section: Introductionmentioning

confidence: 99%

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

2016

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Fish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.

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“…The method provides an optimal hyper plane that separates the different classes (Bosch et al, 2013). The advantage of this method is that it can use a non-linear approach when necessary and therefore in this situation can be more interesting for classification than MLR.…”

Section: Classification Resultsmentioning

confidence: 99%

Monitoring the Mental Status of Football Players

Smets¹,

Joosen²,

Taelman

et al. 2013

Proceedings of the International Congress on Sports Science Research and Technology Support

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This work has tested heart rate to measure anxiety during a penalty shootout. Until now, anxiety is measured through questionnaires, where online monitoring is not possible. Therefore there is a need for physiological parameters to represent anxiety online. Since it is proven that the level of anxiety is a good predictor of penalty outcome, it was hypothesised that this outcome can be estimated with heart rate and activity. To test this hypothesis an experiment has been conducted with 54 participants (age= 23±4,54 years). They each performed three sessions of a penalty shootout, where heart rate and activity were measured. An adapted version of the State-Trait Anxiety Inventory was used as reference for anxiety level. The data have been analysed using a static and dynamic approach. These resulted in parameters that were used to predict the anxiety level and penalty performance of the participant with a multinomial logistic regression model. The results show that 47,11% of the participants were correctly classified into three classes of anxiety. Based on a classification into penalty performance 55,11 % of the participants were correctly classified. It can be concluded that heart rate in combination with activity shows promising results as predictor for anxiety.

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Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile

Cited by 30 publications

References 17 publications

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

Monitoring the Mental Status of Football Players

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