Сравнение эффективности ядер SVM-классификатора для различения пола на основе структурных коннектом
Додонова Ю., Петров Д., Zhukov L. E.
Comparison of the kernel effectiveness of SVM classifier to distinguish gender based on the structural connectome
"Информационные технологии и системы 2015" 39-я междисциплинарная школа-конференция 7 – 11 сентября, Олимпийская деревня, Сочи, Россия
St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015
Machine Learning, Optimization, and Data Science. 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers
Cham : Springer, 2019
This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The 46 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data ...
Added: October 17, 2019
EEML 2019: Experimental Economics and Machine Learning: Proceedings of the Fifth Workshop on Experimental Economics and Machine Learning at the National Research University Higher School of Economics co-located with the Seventh International Conference on Applied Research in Economics (iCare7)
CEUR Workshop Proceedings, 2019
Workshop concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments will be compared with noisy industrial datasets from the web e.g. Insights from behavioral sciences will help data scientists. Behavior scientists will see new inspirations to ...
Added: October 18, 2019
NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines
, , et al., Neuroimage 2020 Vol. 219 No. october P. 1-13
Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, ...
Added: November 12, 2020
Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis.
, , , , in : 2013 IEEE 13th International Conference on Data Mining Workshops. : Los Alamitos : IEEE Computer Society, 2013. Ch. 9.3. P. 440-444.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow ...
Added: February 20, 2014
A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets
, , et al., , in : Lecture Notes in Computer Science. Vol. 6713: Multiple Classifier Systems: 10th International Workshop, MCS 2011, Naples, Italy, June 15-17, 2011. Proceedings.: Springer, 2011. P. 126-136.
It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a ...
Added: March 14, 2016
, , , , in : Intelligent Information and Database Systems: 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7–10, 2021, Proceedings. : Springer, 2021. P. 630-642.
Added: April 6, 2021
, , et al., Бюллетень экспериментальной биологии и медицины 2013 Т. 156 № 11 С. 654-660
В работе приводится формализованная постановка задачи об отборе параметров и построении классификатора для геномной медицинской тест–системы математическими методами машинного обучения без использования специальных биологических и медицинских знаний. Предлагается метод решения данной задачи и обсуждаются результаты апробации этого метода на мирочиповом наборе данных, содержащем информацию о полногеномном транскриптоме образцов эстрогенположительных опухолей молочной железы. Апробация показала, что ...
Added: October 28, 2015
, , et al., , in : PRNI 2016. The 6th International Workshop on Pattern Recognition in Neuroimaging. Trento, Italy, June 22nd – 24th, 2016. : NY : IEEE, 2016. P. 1-4.
The structural connectome classification is a challenging task due to a small sample size and high dimensionality of feature space. In this paper, we propose a new data prepossessing method that combines geometric and topological connectome normalization and significantly improves classification results. We validate this approach by performing classification between autism spectrum disorder and normal ...
Added: March 5, 2017
, , Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2019
We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. Our analysis indicates several new peculiarities of the human connectome (HC). We found that the spectral density of HC has the maximal deviation from the spectral density of the randomized network compared to all other organisms. For many animals ...
Added: November 13, 2018
Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science
Berlin : Springer, 2018
This book constitutes the proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, held in Moscow, Russia, in July 2018. The 29 full papers were carefully reviewed and selected from 107 submissions (of which 26 papers were rejected without being reviewed). The papers are organized in topical sections on ...
Added: September 5, 2018
This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019. The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named: Part I: best ranked papers; machine learning; pattern recognition; ...
Added: September 23, 2019
, , , , in : "Информационные технологии и системы 2015". : St. Petersburg : Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1-15.
We study dierences in structural connectomes between typically developing and autism spectrum disorders individuals with machine learning techniques using connection weights and network metrics as features. We build linear SVM classier with accuracy score 0:64 and report 16 features (seven connection weights and nine network node centralities) best distinguishing these two groups. ...
Added: March 5, 2017
Highly informative marker sets consisting of genes with low individual degree of differential expression
, , et al., Scientific Reports 2015 Vol. 5
Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization ...
Added: October 27, 2015
, , , Управленческий учет и финансы 2019 № 1 С. 2-9
Predicting the impact of news events on changes in the price of financial assets can be used to manage the value of the company. The work demonstrates the possibility of using the method of content analysis to identify the degree of influence of non-financial risks on the market value of a public company. Using four ...
Added: October 31, 2019
, , et al., Frontiers in Psychology 2021 Vol. 11 Article 547353
While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, ...
Added: March 10, 2021
Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression
, , et al., Expert Systems with Applications 2022 Vol. 194 No. May 2022 Article 116553
This paper investigates the forecasting performance for credit default swap (CDS) spreads by Support Vector Machines (SVM), Group Method of Data Handling (GMDH), Long Short-Term Memory (LSTM) and Markov switching autoregression (MSA) for daily CDS spreads of the 513 leading US companies, in the period 2009–2020. The goal of this study is to test the forecasting performance of ...
Added: February 4, 2022
Topological modules of human brain networks are anatomically embedded: evidence from modularity analysis at multiple scales
, , et al., , in : Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics. Vol. 247.: Springer, 2018. P. 299-308.
Human brain networks show modular organization: cortical regions tend to form densely connected modules with only weak inter-modular connections. However, little is known on whether modular structure of brain networks is reliable in terms of test-retest reproducibility and, most importantly, to what extent these topological modules are anatomically embedded. To address these questions, we use ...
Added: December 15, 2017
, , Журнал Новой экономической ассоциации 2012 № 2 С. 27-49
In this work a problem is studied of classification of respondents into classes accepting and not participation in a charity actions. An optimal (in Bayes sense) decisive discriminant rule of division of objects on two classes is constructed for the case when all indicators of observable objects are measured in a nominal scale, and there ...
Added: August 18, 2012
, , Perm University Herald. Economy 2017 Vol. 12 No. 1 P. 49-60
The ability to predict the dynamics of financial instruments is an important topic for financial market players. In the context of large and heterogeneous information, there is a need to use effective methods to data processing for management decision-processing. In particular, machine learning techniques are becoming very popular in financial modeling. The aim of this ...
Added: December 24, 2016
, , , in : Analysis of Images, Social Networks and Texts. Vol. 436: 3rd International Conference on Analysis of Images, Social networks, and Texts.: NY : Springer, 2014. Ch. 19. P. 190-197.
The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood ...
Added: November 21, 2014
Improving prediction of stock market indices by analyzing the psychological states of Twitter users.
, , , Improving prediction of stock market indices by analyzing the psychological states of Twitter users. / Высшая школа экономики. Series WP BRP "Economics/EC". 2013. No. 22.
In our paper, we analyze the possibility of improving the prediction of stock market indicators by conducting a sentiment analysis of Twitter posts. We use a dictionary-based approach for sentiment analysis, which allows us to distinguish eight basic emotions in the tweets of users. We compare the results of applying the Support Vector Machine algorithm ...
Added: December 25, 2013
, , et al., Bulletin of Experimental Biology and Medicine 2014 Vol. 156 No. 5 P. 706-709
The paper presents a formalized statement of the problem of selecting parameters and construction of a genomic classifier for medical test systemswith mathematical methods of machine learning without the use of biological and medical knowledge. A method is proposed to solve this problem. The results of testing the method using microarray datasets containing information on genome-wide transcriptome of the samples of estrogen positive breast ...
Added: October 28, 2015