Literatura académica sobre el tema "STATISTICAL FEATURE RANKING"
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Artículos de revistas sobre el tema "STATISTICAL FEATURE RANKING"
MANSOORI, EGHBAL G. "USING STATISTICAL MEASURES FOR FEATURE RANKING". International Journal of Pattern Recognition and Artificial Intelligence 27, n.º 01 (febrero de 2013): 1350003. http://dx.doi.org/10.1142/s0218001413500031.
Texto completoNaim, Faradila, Mahfuzah Mustafa, Norizam Sulaiman y Zarith Liyana Zahari. "Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals". Traitement du Signal 39, n.º 3 (30 de junio de 2022): 1079–88. http://dx.doi.org/10.18280/ts.390335.
Texto completoSoheili, Majid, Amir-Masoud Eftekhari Moghadam y Mehdi Dehghan. "Statistical Analysis of the Performance of Rank Fusion Methods Applied to a Homogeneous Ensemble Feature Ranking". Scientific Programming 2020 (10 de septiembre de 2020): 1–14. http://dx.doi.org/10.1155/2020/8860044.
Texto completoMogstad, Magne, Joseph Romano, Azeem Shaikh y Daniel Wilhelm. "Statistical Uncertainty in the Ranking of Journals and Universities". AEA Papers and Proceedings 112 (1 de mayo de 2022): 630–34. http://dx.doi.org/10.1257/pandp.20221064.
Texto completoZhang, Zhicheng, Xiaokun Liang, Wenjian Qin, Shaode Yu y Yaoqin Xie. "matFR: a MATLAB toolbox for feature ranking". Bioinformatics 36, n.º 19 (8 de julio de 2020): 4968–69. http://dx.doi.org/10.1093/bioinformatics/btaa621.
Texto completoSADEGHI, SABEREH y HAMID BEIGY. "A NEW ENSEMBLE METHOD FOR FEATURE RANKING IN TEXT MINING". International Journal on Artificial Intelligence Tools 22, n.º 03 (junio de 2013): 1350010. http://dx.doi.org/10.1142/s0218213013500103.
Texto completoNovakovic, Jasmina, Perica Strbac y Dusan Bulatovic. "Toward optimal feature selection using ranking methods and classification algorithms". Yugoslav Journal of Operations Research 21, n.º 1 (2011): 119–35. http://dx.doi.org/10.2298/yjor1101119n.
Texto completoLeguia, Marc G., Zoran Levnajić, Ljupčo Todorovski y Bernard Ženko. "Reconstructing dynamical networks via feature ranking". Chaos: An Interdisciplinary Journal of Nonlinear Science 29, n.º 9 (septiembre de 2019): 093107. http://dx.doi.org/10.1063/1.5092170.
Texto completoWang, W., P. Jones y D. Partridge. "A Comparative Study of Feature-Salience Ranking Techniques". Neural Computation 13, n.º 7 (1 de julio de 2001): 1603–23. http://dx.doi.org/10.1162/089976601750265027.
Texto completoWerner, Tino. "A review on instance ranking problems in statistical learning". Machine Learning 111, n.º 2 (18 de noviembre de 2021): 415–63. http://dx.doi.org/10.1007/s10994-021-06122-3.
Texto completoTesis sobre el tema "STATISTICAL FEATURE RANKING"
Luong, Ngoc Quang. "Word Confidence Estimation and Its Applications in Statistical Machine Translation". Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM051/document.
Texto completoMachine Translation (MT) systems, which generate automatically the translation of a target language for each source sentence, have achieved impressive gains during the recent decades and are now becoming the effective language assistances for the entire community in a globalized world. Nonetheless, due to various factors, MT quality is still not perfect in general, and the end users therefore expect to know how much should they trust a specific translation. Building a method that is capable of pointing out the correct parts, detecting the translation errors and concluding the overall quality of each MT hypothesis is definitely beneficial for not only the end users, but also for the translators, post-editors, and MT systems themselves. Such method is widely known under the name Confidence Estimation (CE) or Quality Estimation (QE). The motivations of building such automatic estimation methods originate from the actual drawbacks of assessing manually the MT quality: this task is time consuming, effort costly, and sometimes impossible in case where the readers have little or no knowledge of the source language. This thesis mostly focuses on the CE methods at word level (WCE). The WCE classifier tags each word in the MT output a quality label. The WCE working mechanism is straightforward: a classifier trained beforehand by a number of features using ML methods computes the confidence score of each label for each MT output word, then tag this word with highest score label. Nowadays, WCE shows an increasing importance in many aspects of MT. Firstly, it assists the post-editors to quickly identify the translation errors, hence improve their productivity. Secondly, it informs readers of portions of sentence that are not reliable to avoid the misunderstanding about the sentence's content. Thirdly, it selects the best translation among options from multiple MT systems. Last but not least, WCE scores can help to improve the MT quality via some scenarios: N-best list re-ranking, Search Graph Re-decoding, etc. In this thesis, we aim at building and optimizing our baseline WCE system, then exploiting it to improve MT and Sentence Confidence Estimation (SCE). Compare to the previous approaches, our novel contributions spread of these following main points. Firstly, we integrate various types of prediction indicators: system-based features extracted from the MT system, together with lexical, syntactic and semantic features to build the baseline WCE systems. We also apply multiple Machine Learning (ML) models on the entire feature set and then compare their performances to select the optimal one to optimize. Secondly, the usefulness of all features is deeper investigated using a greedy feature selection algorithm. Thirdly, we propose a solution that exploits Boosting algorithm as a learning method in order to strengthen the contribution of dominant feature subsets to the system, thus improve of the system's prediction capability. Lastly, we explore the contributions of WCE in improving MT quality via some scenarios. In N-best list re-ranking, we synthesize scores from WCE outputs and integrate them with decoder scores to calculate again the objective function value, then to re-order the N-best list to choose a better candidate. In the decoder's search graph re-decoding, the proposition is to apply WCE score directly to the nodes containing each word to update its cost regarding on the word quality. Furthermore, WCE scores are used to build useful features, which can enhance the performance of the Sentence Confidence Estimation system. In total, our work brings the insightful and multidimensional picture of word quality prediction and its positive impact on various sectors for Machine Translation. The promising results open up a big avenue where WCE can play its role, such as WCE for Automatic Speech Recognition (ASR) System, WCE for multiple MT selection, and WCE for re-trainable and self-learning MT systems
Peel, Thomas. "Algorithmes de poursuite stochastiques et inégalités de concentration empiriques pour l'apprentissage statistique". Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4769/document.
Texto completoThe first part of this thesis introduces new algorithms for the sparse encoding of signals. Based on Matching Pursuit (MP) they focus on the following problem : how to reduce the computation time of the selection step of MP. As an answer, we sub-sample the dictionary in line and column at each iteration. We show that this theoretically grounded approach has good empirical performances. We then propose a bloc coordinate gradient descent algorithm for feature selection problems in the multiclass classification setting. Thanks to the use of error-correcting output codes, this task can be seen as a simultaneous sparse encoding of signals problem. The second part exposes new empirical Bernstein inequalities. Firstly, they concern the theory of the U-Statistics and are applied in order to design generalization bounds for ranking algorithms. These bounds take advantage of a variance estimator and we propose an efficient algorithm to compute it. Then, we present an empirical version of the Bernstein type inequality for martingales by Freedman [1975]. Again, the strength of our result lies in the variance estimator computable from the data. This allows us to propose generalization bounds for online learning algorithms which improve the state of the art and pave the way to a new family of learning algorithms taking advantage of this empirical information
KUMAR, AKHIL. "MACHINE LEARNING BASED INTRUSION DETECTION SYSTEM USING STATISTICAL FEATURE RANKING METHOD". Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19866.
Texto completoCapítulos de libros sobre el tema "STATISTICAL FEATURE RANKING"
Kamarainen, J. K., J. Ilonen, P. Paalanen, M. Hamouz, H. Kälviäinen y J. Kittler. "Object Evidence Extraction Using Simple Gabor Features and Statistical Ranking". En Image Analysis, 119–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_14.
Texto completoZhang, Xueyan, Yixuan Zhang, Ye Yang, Chengcheng Deng y Jun Yang. "Uncertainty Analysis and Sensitivity Evaluation of a Main Steam Line Break Accident on an Advanced PWR". En Springer Proceedings in Physics, 327–41. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_30.
Texto completoÉrdi, Péter. "Choices, games, laws, and the Web". En Ranking, 65–98. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190935467.003.0004.
Texto completoKastrati, Zenun, Ali Shariq Imran y Sule Yildirim Yayilgan. "A Hybrid Concept Learning Approach to Ontology Enrichment". En Innovations, Developments, and Applications of Semantic Web and Information Systems, 85–119. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5042-6.ch004.
Texto completoJovančić, Predrag D., Miloš Tanasijević, Vladimir Milisavljević, Aleksandar Cvjetić, Dejan Ivezić y Uglješa Srbislav Bugarić. "Applying the Fuzzy Inference Model in Maintenance Centered to Safety". En Advances in Civil and Industrial Engineering, 142–65. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3904-0.ch009.
Texto completoActas de conferencias sobre el tema "STATISTICAL FEATURE RANKING"
Sharma, Yash, Somya Sharma y Anshul Arora. "Feature Ranking using Statistical Techniques for Computer Networks Intrusion Detection". En 2022 7th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2022. http://dx.doi.org/10.1109/icces54183.2022.9835831.
Texto completoTuan, Pham Minh, Nguyen Linh Trung, Mouloud Adel y Eric Guedj. "AutoEncoder-based Feature Ranking for Predicting Mild Cognitive Impairment Conversion using FDG-PET Images". En 2023 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2023. http://dx.doi.org/10.1109/ssp53291.2023.10208072.
Texto completoKumar, Akhil y Shailender Kumar. "Intrusion detection based on machine learning and statistical feature ranking techniques". En 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2023. http://dx.doi.org/10.1109/confluence56041.2023.10048802.
Texto completoWang, Min, Xipeng Chen, Wentao Liu, Chen Qian, Liang Lin y Lizhuang Ma. "DRPose3D: Depth Ranking in 3D Human Pose Estimation". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/136.
Texto completoTamilarasan, A., S. Mukkamala, A. H. Sung y K. Yendrapalli. "Feature Ranking and Selection for Intrusion Detection Using Artificial Neural Networks and Statistical Methods". En The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247131.
Texto completoBahrami, Peyman y Lesley A. James. "Field Production Optimization Using Smart Proxy Modeling; Implementation of Sequential Sampling, Average Feature Ranking, and Convolutional Neural Network". En SPE Canadian Energy Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212809-ms.
Texto completoForeman, Geoff, Steven Bott, Jeffrey Sutherland y Stephan Tappert. "The Development and Use of an Absolute Depth Size Specification in ILI-Based Crack Integrity Management of Pipelines". En 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64224.
Texto completoIdogun, Akpevwe Kelvin, Ruth Oyanu Ujah y Lesley Anne James. "Surrogate-Based Analysis of Chemical Enhanced Oil Recovery – A Comparative Analysis of Machine Learning Model Performance". En SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208452-ms.
Texto completoAdl, Amin Ahmadi, Xiaoning Qian, Ping Xu, Kendra Vehik y Jeffrey P. Krischer. "Feature ranking based on synergy networks to identify prognostic markers in DPT-1". En 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2012. http://dx.doi.org/10.1109/gensips.2012.6507728.
Texto completoLen, Przemysław. "The Use Of Statistical Methods in Creation of the Urgency Ranking of the Land Consolidation and Land Exchange Works". En Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.212.
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