| 50951. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks
- 2001
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Ingår i: Journal of Intelligent & Fuzzy Systems. - Amsterdam : IOS Press. - 1064-1246 .- 1875-8967. ; 10:2, s. 117-130
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Tidskriftsartikel (refereegranskat)abstract
- An approach to detecting colour specks in an image taken from a pulp sample of recycled paper is presented. The task is solved through pixel-wise colour classification by an artificial neural network and post-processing based on the evidence theory. The network is trained using possibilistic target values, which are determined through a self-organising process in a 2D and 1D map of chromaticity and lightness, respectively. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.
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| 50952. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Electromyographic Patterns during Golf Swing : Activation Sequence Profiling and Prediction of Shot Effectiveness
- 2016
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 16:4
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Tidskriftsartikel (refereegranskat)abstract
- This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player’s performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.
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| 50953. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Hybrid and ensemble-based soft computing techniques in bankruptcy prediction : a survey
- 2010
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Ingår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications. - Heidelberg : Springer. - 1432-7643 .- 1433-7479. ; 14:9, s. 995-1010
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.
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| 50954. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images
- 2006
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Ingår i: International journal of pattern recognition and artificial intelligence. - Singapore : World Scientific. - 0218-0014. ; 20:8, s. 1187-1205
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Tidskriftsartikel (refereegranskat)abstract
- An approach to integrating the global and local kernel-based automated analysis of vocal fold images aiming to categorize laryngeal diseases is presented in this paper. The problem is treated as an image analysis and recognition task. A committee of support vector machines is employed for performing the categorization of vocal fold images into healthy, diffuse and nodular classes. Analysis of image color distribution, Gabor filtering, cooccurrence matrices, analysis of color edges, image segmentation into homogeneous regions from the image color, texture and geometry view point, analysis of the soft membership of the regions in the decision classes, the kernel principal components based feature extraction are the techniques employed for the global and local analysis of laryngeal images. Bearing in mind the high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 vocal fold images is rather encouraging.
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| 50955. |
- Verikas, Antanas, et al.
(författare)
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Leverages Based Neural Networks Fusion
- 2004
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Ingår i: Neural information processing. - Berlin, Heidelberg : Springer Berlin Heidelberg. ; , s. 446-451
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Konferensbidrag (refereegranskat)abstract
- To improve estimation results, outputs of multiple neural networks can be aggregated into a committee output. In this paper, we study the usefulness of the leverages based information for creating accurate neural network committees. Based on the approximate leave-one-out error and the suggested, generalization error based, diversity test, accurate and diverse networks are selected and fused into a committee using data dependent aggregation weights. Four data dependent aggregation schemes – based on local variance, covariance, Choquet integral, and the generalized Choquet integral – are investigated. The effectiveness of the approaches is tested on one artificial and three real world data sets.
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| 50956. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting neural networks for a committee decision
- 2002
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Ingår i: International Journal of Neural Systems. - Singapore : World Scientific. - 0129-0657 .- 1793-6462. ; 12:5, s. 351-361
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Tidskriftsartikel (refereegranskat)abstract
- To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.
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| 50957. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting neural networks for making a committee decision
- 2002
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Ingår i: ARTIFICIAL NEURAL NETWORKS - ICANN 2002. - Berlin : Springer Berlin/Heidelberg. - 9783540440741 - 9783540460848 ; , s. 420-425
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Konferensbidrag (refereegranskat)abstract
- To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The effectiveness of the approach is demonstrated on two artificial and three real data sets.
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| 50958. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting salient features for classification committees
- 2003
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Ingår i: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. - Heidelberg : Springer Berlin/Heidelberg. - 9783540404088 - 9783540449898 ; , s. 35-42
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Konferensbidrag (refereegranskat)abstract
- We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. By contrast, the accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features. © Springer-Verlag Berlin Heidelberg 2003.
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| 50959. |
- Verikas, Antanas, 1951-, et al.
(författare)
-
Soft combination of neural classifiers : a comparative study
- 1999
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Ingår i: Pattern Recognition Letters. - Amsterdam : Elsevier. - 0167-8655 .- 1872-7344. ; 20:4, s. 429-444
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents four schemes for soft fusion of the outputs of multiple classifiers. In the first three approaches, the weights assigned to the classifiers or groups of them are data dependent. The first approach involves the calculation of fuzzy integrals. The second scheme performs weighted averaging with data-dependent weights. The third approach performs linear combination of the outputs of classifiers via the BADD defuzzification strategy. In the last scheme, the outputs of multiple classifiers are combined using Zimmermann's compensatory operator. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data-dependent weights, compared to various existing combination schemes of multiple classifiers.
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| 50960. |
- Verikas, Antanas, 1951-, et al.
(författare)
-
Training neural networks by stochastic optimisation
- 2000
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Ingår i: Neurocomputing. - Amsterdam : Elsevier. - 0925-2312 .- 1872-8286. ; 30:1-4, s. 153-172
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Tidskriftsartikel (refereegranskat)abstract
- We present a stochastic learning algorithm for neural networks. The algorithm does not make any assumptions about transfer functions of individual neurons and does not depend on a functional form of a performance measure. The algorithm uses a random step of varying size to adapt weights. The average size of the step decreases during learning. The large steps enable the algorithm to jump over local maxima/minima, while the small ones ensure convergence in a local area. We investigate convergence properties of the proposed algorithm as well as test the algorithm on four supervised and unsupervised learning problems. We have found a superiority of this algorithm compared to several known algorithms when testing them on generated as well as real data.
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