Relevant bibliographies by topics / SVM

Academic literature on the topic 'SVM'

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Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "SVM"

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Wang, Bo, Yu Kai Yao, Xiao Ping Wang, and Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM." Applied Mechanics and Materials 701-702 (December 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.

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As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier. This paper proposes a new ensemble algorithm based on SVM. The proposed classification algorithm PB-SVM Ensemble consists of some SVM classifiers produced by PCAenSVM and fifty classifiers trained using Bagging, the results are combined to make the final decision on testing set using majority voting. The performance of PB-SVM Ensemble are evaluated on six datasets which are from UCI repository, Statlog or the famous research. The results of the experiment are compared with LibSVM, PCAenSVM and Bagging. PB-SVM Ensemble outperform other three algorithms in classification accuracy, and at the same time keep a higher confidence of accuracy than Bagging.
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ZHU, Yongsheng. "A new type SVM??projected SVM." Science in China Series G 47, no. 7 (2004): 21. http://dx.doi.org/10.1360/03yb0244.

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Huang, Wencheng, Hongyi Liu, Yue Zhang, Rongwei Mi, Chuangui Tong, Wei Xiao, and Bin Shuai. "Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM." Applied Soft Computing 109 (September 2021): 107541. http://dx.doi.org/10.1016/j.asoc.2021.107541.

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Yanling, Xu, Wu Baolin, and Baolin Liushan. "A Network-Adapative SVC Streaming Strategy with SVM-Based Bandwidth Prediction." International Journal of Future Computer and Communication 3, no. 3 (2014): 205–9. http://dx.doi.org/10.7763/ijfcc.2014.v3.297.

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SHIMADA, KAZUTAKA, KOJI HAYASHI, and TSUTOMU ENDO. "Product Specification Extraction Using SVM and Transductive SVM." Journal of Natural Language Processing 12, no. 3 (2005): 43–66. http://dx.doi.org/10.5715/jnlp.12.3_43.

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Huang, Min-Wei, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke, and Chih-Fong Tsai. "SVM and SVM Ensembles in Breast Cancer Prediction." PLOS ONE 12, no. 1 (January 6, 2017): e0161501. http://dx.doi.org/10.1371/journal.pone.0161501.

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Lapin, Maksim, Matthias Hein, and Bernt Schiele. "Learning using privileged information: SVM+ and weighted SVM." Neural Networks 53 (May 2014): 95–108. http://dx.doi.org/10.1016/j.neunet.2014.02.002.

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Safiya, K. M. "Genetic Algorithm with SRM SVM Classifier for Face Verification." International Journal of Computer Science and Information Technology 4, no. 4 (August 31, 2012): 151–63. http://dx.doi.org/10.5121/ijcsit.2012.4414.

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Deepthi, Medechal, Mosali Harini, Pandiri Sai Geethika, Vusirikala Kalyan, and K. Kishor. "Data Classification of Dark Web using SVM and S3VM." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (September 30, 2023): 510–17. http://dx.doi.org/10.22214/ijraset.2023.55643.

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Abstract: There are many issues regarding the dark web structural Type. It also increases the number of cybercrimes like illegal trade, forums, Terrorist activity. By understanding online criminal’s actions are challenging because the data is available in a very great extent amount. In a recent day the Online crimes are increasing all over the world. The data related to different types of frauds and scams, such as phishing schemes, identity theft etc. The data and discussion related to the act of hacking (hacktivist) activities, this often involve political or social causes. In some parts of dark web might be used for anonymous communication and the losing of sensitive information to explore wrong doing by governments or corporations. But in some countries the dark web might be used as a means to access information and content that is hardly restricted. The primary focus of this research is to develop a hybrid classification model that combines the strengths of deep learning and natural language processing algorithms. The model leverages a curated dataset of Dark Web content, meticulously labeled by content category, ranging from illegal commerce to cyber threats. By extracting relevant features from the textual and visual components of the data, the model demonstrates superior accuracy in distinguishing between different content categories
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Ardjani, Fatima, and Kaddour Sadouni. "Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)." International Journal of Modern Education and Computer Science 2, no. 2 (December 16, 2010): 32–38. http://dx.doi.org/10.5815/ijmecs.2010.02.05.

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Dissertations / Theses on the topic "SVM"

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Guermeur, Yann. "SVM Multiclasses, Théorie et Applications." Habilitation à diriger des recherches, Université Henri Poincaré - Nancy I, 2007. http://tel.archives-ouvertes.fr/tel-00203086.

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Les machines à vecteurs support (SVM) sont des modèles de l'apprentissage automatique qui font actuellement l'objet de nombreux travaux de recherche, ceci pour deux raisons principales : d'une part,
leurs performances constituent l'état de l'art dans de multiples domaines
de la reconnaissance des formes, d'autre part, elles possèdent des propriétés statistiques remarquables. Le premier modèle de SVM proposé par Vapnik et ses co-auteurs calcule des dichotomies. Il peut être utilisé pour effectuer des tâches de discrimination à catégories multiples, dans le cadre de l'application de méthodes de décomposition. Des SVM multi-classes ont également été proposées dans la littérature, parmi lesquelles nous distinguons celles qui s'appuient sur un modèle affine multivarié, que nous nommons M-SVM. Ce mémoire se présente comme une étude synthétique de la discrimination à catégories multiples au moyen de SVM. Il se concentre plus particulièrement sur l'analyse des M-SVM.

Le chapitre deux est consacré à la description des SVM multi-classes,
à leur mise en oeuvre et à l'analyse de leurs performances. Nous présentons successivement le cadre théorique de leur étude, les différents modèles, une étude théorique de leurs performances en généralisation, leur programmation ainsi que les différentes méthodes de sélection de modèle qui leur sont dédiées. Le chapitre trois décrit une application de la M-SVM de Weston et Watkins en biologie structurale prédictive. Le problème traité est la prédiction de la structure secondaire des protéines globulaires.
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Krontorád, Jan. "Implementace algoritmu SVM v FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236773.

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This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their implementation in FPGA. There are basics about classifiers and learning. Two learning algorithms are introduced SMO algorithm and one hardware-friendly algorithm.
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Synek, Radovan. "Klasifikace textu pomocí metody SVM." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237229.

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This thesis deals with text mining. It focuses on problems of document classification and related techniques, mainly data preprocessing. Project also introduces the SVM method, which has been chosen for classification, design and testing of implemented application.
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MELONI, RAPHAEL BELO DA SILVA. "REMOTE SENSING IMAGE CLASSIFICATION USING SVM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31439@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Classificação de imagens é o processo de extração de informação em imagens digitais para reconhecimento de padrões e objetos homogêneos, que em sensoriamento remoto propõe-se a encontrar padrões entre os pixels pertencentes a uma imagem digital e áreas da superfície terrestre, para uma análise posterior por um especialista. Nesta dissertação, utilizamos a metodologia de aprendizado de máquina support vector machines para o problema de classificação de imagens, devido a possibilidade de trabalhar com grande quantidades de características. Construímos classificadores para o problema, utilizando imagens distintas que contém as informações de espaços de cores RGB e HSB, dos valores altimétricos e do canal infravermelho de uma região. Os valores de relevo ou altimétricos contribuíram de forma excelente nos resultados, uma vez que esses valores são características fundamentais de uma região e os mesmos não tinham sido analisados em classificação de imagens de sensoriamento remoto. Destacamos o resultado final, do problema de classificação de imagens, para o problema de identificação de piscinas com vizinhança dois. Os resultados obtidos são 99 por cento de acurácia, 100 por cento de precisão, 93,75 por cento de recall, 96,77 por cento de F-Score e 96,18 por cento de índice Kappa.
Image Classification is an information extraction process in digital images for pattern and homogeneous objects recognition. In remote sensing it aims to find patterns from digital images pixels, covering an area of earth surface, for subsequent analysis by a specialist. In this dissertation, to this images classification problem we employ Support Vector Machines, a machine learning methodology, due the possibility of working with large quantities of features. We built classifiers to the problem using different image information, such as RGB and HSB color spaces, altimetric values and infrared channel of a region. The altimetric values contributed to excellent results, since these values are fundamental characteristics of a region and they were not previously considered in remote sensing images classification. We highlight the final result, for the identifying swimming pools problem, when neighborhood is two. The results have 99 percent accuracy, 100 percent precision, 93.75 percent of recall, 96.77 percent F-Score and 96.18 percent of Kappa index.
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Chen, Kathy F. (Kathy Fang-Yun). "Offline and online SVM performance analysis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41259.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 51-52).
To understand and evaluate the performance of a machine learning algorithm, the Support Vector Machine, this thesis compares the strengths and weaknesses between the offline and online SVM. The work includes the performance comparisons of SVMLight and LaSVM, with results of training time, number of support vectors, kernel evaluations, and test accuracies. Multiple datasets are experimented to cover a wide range of input data and training problems. Overall, the online LaSVM has trained with less time and returned comparable test accuracies than SVMLight. A general breakdown of the two algorithms and their computation efforts are included for detailed analysis.
by Kathy F. Chen.
M.Eng.
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TEIXEIRA, JÚNIOR Talisman Cláudio de Queiroz. "Classificação fonética utilizando Boosting e SVM." Universidade Federal do Pará, 2006. http://repositorio.ufpa.br/jspui/2011/2533.

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Para compor um sistema de Reconhecimento Automático de Voz, pode ser utilizada uma tarefa chamada Classificação Fonética, onde a partir de uma amostra de voz decide-se qual fonema foi emitido por um interlocutor. Para facilitar a classificação e realçar as características mais marcantes dos fonemas, normalmente, as amostras de voz são pré- processadas através de um fronl-en'L Um fron:-end, geralmente, extrai um conjunto de parâmetros para cada amostra de voz. Após este processamento, estes parâmetros são insendos em um algoritmo classificador que (já devidamente treinado) procurará decidir qual o fonema emitido. Existe uma tendência de que quanto maior a quantidade de parâmetros utilizados no sistema, melhor será a taxa de acertos na classificação. A contrapartida para esta tendência é o maior custo computacional envolvido. A técnica de Seleção de Parâmetros tem como função mostrar quais os parâmetros mais relevantes (ou mais utilizados) em uma tarefa de classificação, possibilitando, assim, descobrir quais os parâmetros redundantes, que trazem pouca (ou nenhuma) contribuição à tarefa de classificação. A proposta deste trabalho é aplicar o classificador SVM à classificação fonética, utilizando a base de dados TIMIT, e descobrir os parâmetros mais relevantes na classificação, aplicando a técnica Boosting de Seleção de Parâmetros.
With the aim of setting up a Automatic Speech Recognition (ASR) system, a task named Phonetic Classification can be used. That task consists in, from a speech sample, deciding which phoneme was pronounced by a speaker. To ease the classification task and to enhance the most marked characteristics of the phonemes, the speech samples are usually pre-processed by a front-end. A front-end, as a general rule, extracts a set of features to each speech sample. After that, these features are inserted in a classification algorithm, that (already properly trained) will try to decide which phoneme was pronounced. There is a rule of thumb which says that the more features the system uses, the smaller the classification error rate will be. The disadvantage to that is the larger computational cost. Feature Selection task aims to show which are the most relevant (or more used) features in a classification task. Therefore, it is possible to discover which are the redundant features, that make little (or no) contribution to the classification task. The aim of this work is to apply SVM classificator in Phonetic Classification task, using TIMIT database, and discover the most relevant features in this classification using Boosting approach to implement Feature Selection.
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Štechr, Vladislav. "Využití SVM v prostředí finančních trhů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241651.

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This thesis deals with use of regression or classification based on support vector machines from machine learning field. SVMs predict values that are used for decisions of automatic trading system. Regression and classification are evaluated for their usability for decision making. Strategy is being then optimized, tested and evaluated on foreign exchange market Forex historic data set. Results are promising. Strategy could be used in combination with other strategy that would confirm decisions for entering and exiting trades.
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Yao, Xiaojun. "Méthodes Non-linéaires (ANNs, SVMs) : applications à la Classification et à la Corrélation des Propriétés Physicochimiques et Biologiques." Paris 7, 2004. http://www.theses.fr/2004PA077182.

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Sýkora, Michal. "Automatické označování obrázků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236453.

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This work focuses on automatic classification of images into semantic classes based on their contentc, especially in using SVM classifiers. The main objective of this work is to improve classification accuracy on large datasets. Both linear and nonlinear SVM classifiers are considered. In addition, the possibility of transforming features by Restricted Boltzmann Machines and using linear SVM is explored as well. All these approaches are compared in terms of accuracy, computational demands, resource utilization, and possibilities for future research.
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xu, wei. "SVM-based algorithms for aligning ontologies using literature." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15974.

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Ontologies is one of the key techniques used in Semantic Web establishment. Nowadays，many ontologies have been developed and it is critical to understand the relationships between the terms of the ontologies, i.e. we need to align the ontologies.

This thesis deals with an approach for finding relationships between ontologies using literature by classifying documents related to terms in the ontologies.

 

In this project the general method from [1] is used, but in the classifier generation part, a brand new classifier based on SVMs algorithm is implemented by LPU and SVMlight. We evaluate our approach and compare it to previous approaches.

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Books on the topic "SVM"

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S, Triandofilidi R., and Aĭvazi͡a︡n Sergeĭ Artemʹevich, eds. Rukovodstvo po operat͡s︡ionnoĭ sisteme SVM ES. Moskva: T͡S︡entr. ėkonomiko-matematicheskiĭ in-t Akademii nauk SSSR, 1988.

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SvM: Die Festschrift : für Stanislaus von Moos. Zürich: gta Verlag, 2005.

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Alekseev, A. V. Programmirovanie v podsisteme dialogovoĭ obrabotki SVM ES: A.V. Alekseev, D.D. Gorbatenko, A.V. Serzhantov. Moskva: "Radio i svi͡a︡zʹ", 1990.

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-W, Lee S., and Verri Alessandro, eds. Pattern recognition with support vector machines: First international workshop, SVM 2002, Niagara Falls, Canada, August 202 : proceedings. Berlin: Springer, 2002.

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Germany), SVM (2008 Munich. Systems and virtualization management: Standards and new technologies : second international workshop, SVM 2008, Munich, Germany, October 21-22, 2008, proceedings. Berlin: Springer, 2008.

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Germany), SVM (2008 Munich. Systems and virtualization management: Standards and new technologies : second international workshop, SVM 2008, Munich, Germany, October 21-22, 2008, proceedings. Berlin: Springer, 2008.

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Othmar, Marti, and Amrein Matthias, eds. STM and SFM in biology. San Diego: Academic Press, 1993.

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Arko, Andraž, and Jan Dominik Bogataj. Sem mislil da sem sam: Hagiografska drama o mučencu Lojzetu Grozdetu. Ljubljana: Založba Brat Frančišek, 2012.

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Fisher, Roger. Como chegar ao sim: Negociação de acordos sem concessões. 2nd ed. Rio de Janeiro: Imago, 2005.

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Križanec, Siniša. Svi bute me tužili, ili, "Kako sam pomirio duhove". Zagreb: ITD d.o.o, 1999.

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Book chapters on the topic "SVM"

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Beney, Jean, and Cornelis H. A. Koster. "SVM Paradoxes." In Perspectives of Systems Informatics, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11486-1_8.

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Yang, Yuli, Zhi Li, and Yanfeng Wang. "Risk Prediction of Esophageal Cancer Using SOM Clustering, SVM and GA-SVM." In Communications in Computer and Information Science, 345–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3415-7_29.

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Parand, Kourosh, Fatemeh Baharifard, Alireza Afzal Aghaei, and Mostafa Jani. "Basics of SVM Method and Least Squares SVM." In Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines, 19–36. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6553-1_2.

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Wu, Chao-Chin, De-Xang Wang, and Lien-Fu Lai. "Accelerate SVM Training with OHD-SVM on GPU." In Big Data – BigData 2023, 209–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44725-9_15.

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Murty, M. N., and Rashmi Raghava. "Kernel-Based SVM." In Support Vector Machines and Perceptrons, 57–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41063-0_5.

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Matías, José M. "Partially Parametric SVM." In Progress in Artificial Intelligence, 67–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595014_7.

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Veisi, Hadi. "Introduction to SVM." In Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines, 3–18. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6553-1_1.

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Yu, Hwanjo, Youngdae Kim, and Seungwon Hwang. "RV-SVM: An Efficient Method for Learning Ranking SVM." In Advances in Knowledge Discovery and Data Mining, 426–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_39.

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Das, Banee Bandana, Saswat Kumar Ram, Bibudhendu Pati, Chhabi Rani Panigrahi, Korra Sathya Babu, and Ramesh Kumar Mohapatra. "SVM and Ensemble-SVM in EEG-Based Person Identification." In Advances in Intelligent Systems and Computing, 137–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6353-9_13.

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Verma, Praveen, Tushar Bhardwaj, Abhay Bhatia, and Mohd Mursleen. "Sentiment Analysis “Using SVM, KNN and SVM with PCA”." In Artificial Intelligence in Cyber Security: Theories and Applications, 35–53. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28581-3_5.

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Conference papers on the topic "SVM"

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Fink, Eric, Jaroslaw Kwapisz, and Ioannis Roudas. "Optimized SVM constellations for SDM fibers." In 2021 IEEE Photonics Conference (IPC). IEEE, 2021. http://dx.doi.org/10.1109/ipc48725.2021.9593013.

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Sun, Mingshun, Yanmao Man, Ming Li, and Ryan Gerdes. "SVM." In WiSec '20: 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3395351.3399348.

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Ludwig, Oswaldo, Cristiano Premebida, Urbano Nunes, and Rui Araujo. "Evaluation of Boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection." In 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6082909.

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Cai, Hong, and Yufeng Wang. "Transcriptomic analysis using SVD clustering and SVM classification." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169476.

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Li, Zhongguo, Jie Hou, Qi Wang, and Qinghua Liu. "Road type recognition based on SOM and SVM." In 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2011. http://dx.doi.org/10.1109/cecnet.2011.5768757.

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Fan, Yan-Feng, De-Xian Zhang, and Hua-Can He. "Tangent Circular Arc Smooth SVM (TCA-SSVM) Research." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.112.

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Shalev-Shwartz, Shai, and Nathan Srebro. "SVM optimization." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390273.

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Stanley-Marbell, Phillip. "Sal/Svm." In Virtual Machines and Intermediate Languages. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1941054.1941055.

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Chang, Qingqing, Shaofu Lin, and Xiliang Liu. "Stacked-SVM." In ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377713.3377735.

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"VISUAL SVM." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002521003090314.

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Reports on the topic "SVM"

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Davenport, Mark A. The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM. Fort Belvoir, VA: Defense Technical Information Center, December 2005. http://dx.doi.org/10.21236/ada486719.

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Carin, Lawrence. ICA Feature Extraction and SVM Classification of FLIR Imagery. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441506.

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Morris, Brendan, David W. Aha, Bryan Auslander, and Kalyan Gupta. Learning and Leveraging Context for Maritime Threat Analysis: Vessel Classification using Exemplar-SVM. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574666.

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Li, Qi. Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6614.

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Karypis, George. Better Kernels and Coding Schemes Lead to Improvements in SVM-Based Secondary Structure Prediction. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada439626.

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Naseem, Shahid. Hand written digits classification and recognition using convolutional neural networks by implementing the techniques of MLP and SVM. Peeref, March 2023. http://dx.doi.org/10.54985/peeref.2303p8226220.

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, December 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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Brownlee, N. SVG Drawings for RFCs: SVG 1.2 RFC. RFC Editor, December 2016. http://dx.doi.org/10.17487/rfc7996.

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Ling, Alice V., András Vládar, Bradley N. Damazo, M. Alkan Donmez, and Michael T. Postek. SEM Sentinel:. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6498.

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Zyphur, Michael. Intermediate SEM in Stata: From CFA to SEM. Instats Inc., 2022. http://dx.doi.org/10.61700/9qo0ssbbzp4nl469.

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This seminar introduces the Stata ‘sem’ latent variable modeling framework and explores measurement models including bi-factor and hierarchical factor models and scale reliability in CFA, as well as SEMs with latent variable interactions (moderation), indirect effects (mediation), latent conditional indirect effects (moderated mediation), and latent instrumental variable methods in an SEM framework (IV-SEM). An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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