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Journal articles on the topic 'Incremental Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Poesio, Massimo, and Hannes Rieser. "An Incremental Model of Anaphora and Reference Resolution Based on Resource Situations." Dialogue & Discourse 2, no. 1 (May 3, 2011): 235–77. http://dx.doi.org/10.5087/dad.2011.110.

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Notwithstanding conclusive psychological and corpus evidence that at least some aspects of anaphoric and referential interpretation take place incrementally, and the existence of some computational models of incremental reference resolution, many aspects of the linguistics of incremental reference interpretation still have to be better understood. We propose a model of incremental reference interpretation based on Loebner’s theory of definiteness and on the theory of anaphoric accessibility via resource situations developed in Situation Semantics, and show how this model can account for a variety of psychological results about incremental reference interpretation.

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2

Zubair Asghar, Muhammad, Aurangzeb Khan, Fazal Masud Kundi, Maria Qasim, Furqan Khan, Rahman Ullah, and Irfan Ullah Nawaz. "Medical opinion lexicon: an incremental model for mining health reviews." International Journal of Academic Research 6, no. 1 (January 30, 2014): 295–302. http://dx.doi.org/10.7813/2075-4124.2014/6-1/a.39.

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3

Maulana, Harry Fajar, Sry Mayunita, Hastuti Hastuti, and Andy Arya Maulana Wijaya. "Diskurusus Kebijakan Publik Model Incremental." Kybernan: Jurnal Studi Kepemerintahan 3, no. 1 (September 1, 2018): 1–13. http://dx.doi.org/10.35326/kybernan.v3i1.330.

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Salah satu kesulitan dalam kebijakan publik adalah efisiensi waktu, dimanakebijakan akan selalu melibatkan waktu, tenaga dan materi yang cukup panjang. Artikel inibertujuan untuk memberikan beberapa perspektif untuk medeskripsikan usahapembuatan kebijakan publik dengan sederhana. Melalui pendekatan inkremental, artikelyang didasarkan pada studi pustaka ini mencoba mengurai berbagai persepektif tentangkebijakan publik model inkremental. Maka, artikel ini menemukan bahwa modelInkremental dapat dinyatakan sebagai sebuah model kebijakan yang dilakukan denganmendesain ulang kebijakan yang ada namun masih dalam koridor rangka utama kebijakanasalnya. Model Inkremental dilakukan untuk menghadapi masalah yang membutuhkanpenanganan dengan waktu yang cukup singkat. Tantangannya adalah dalam modelkebijakan ini, seringkali membutuhkan ketelitian aktor kebijakan dan pilihan-pilihanalternatif yang tidak mudah.

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Yang, Chuan-sheng, Yu-jia Zheng, and Chao Wang. "Incremental multivariate Markov chain model." Journal of Engineering 2018, no. 16 (November 1, 2018): 1433–35. http://dx.doi.org/10.1049/joe.2018.8278.

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Uchitel, Sebastian, Dalal Alrajeh, Shoham Ben-David, Victor Braberman, Marsha Chechik, Guido De Caso, Nicolas D’Ippolito, et al. "Supporting incremental behaviour model elaboration." Computer Science - Research and Development 28, no. 4 (October 25, 2012): 279–93. http://dx.doi.org/10.1007/s00450-012-0233-1.

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6

Wang, Jingjing, Wenjun Jiang, Kenli Li, Guojun Wang, and Keqin Li. "Incremental Group-Level Popularity Prediction in Online Social Networks." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–26. http://dx.doi.org/10.1145/3461839.

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Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

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7

Giese, Holger, and Robert Wagner. "From model transformation to incremental bidirectional model synchronization." Software & Systems Modeling 8, no. 1 (March 28, 2008): 21–43. http://dx.doi.org/10.1007/s10270-008-0089-9.

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8

de Carvalho, Sergio E. R., and Toacy C. de Oliveirae. "An Incremental Model for Concurrent Objects." Electronic Notes in Theoretical Computer Science 14 (1998): 86–93. http://dx.doi.org/10.1016/s1571-0661(05)80231-2.

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9

Krishnamurthi, Shriram, and Kathi Fisler. "Foundations of incremental aspect model-checking." ACM Transactions on Software Engineering and Methodology 16, no. 2 (April 2007): 7. http://dx.doi.org/10.1145/1217295.1217296.

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10

Potts, Duncan, and Claude Sammut. "Incremental Learning of Linear Model Trees." Machine Learning 61, no. 1-3 (June 9, 2005): 5–48. http://dx.doi.org/10.1007/s10994-005-1121-8.

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11

Pinto, Rafael Coimbra, and Paulo Martins Engel. "A Fast Incremental Gaussian Mixture Model." PLOS ONE 10, no. 10 (October 7, 2015): e0139931. http://dx.doi.org/10.1371/journal.pone.0139931.

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12

Molnár, Vince, András Vörös, Dániel Darvas, Tamás Bartha, and István Majzik. "Component-wise incremental LTL model checking." Formal Aspects of Computing 28, no. 3 (January 4, 2016): 345–79. http://dx.doi.org/10.1007/s00165-015-0347-x.

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13

Rostamabadi, Farshad, and Mohammad Ghodsi. "Incremental labeling in closed-2PM model." Computers & Electrical Engineering 36, no. 5 (September 2010): 895–901. http://dx.doi.org/10.1016/j.compeleceng.2008.04.012.

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14

Shkutin, L. I. "Incremental deformation model for a rod." Journal of Applied Mechanics and Technical Physics 40, no. 4 (July 1999): 757–62. http://dx.doi.org/10.1007/bf02468455.

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Shkutin, L. I. "Incremental deformation model for a shell." Journal of Applied Mechanics and Technical Physics 40, no. 5 (September 1999): 957–61. http://dx.doi.org/10.1007/bf02468483.

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16

Huang, Qiuyan, Qingzhong Li, Hong Li, and Zhongmin Yan. "An Approach to Incremental Deep Web Crawling Based on Incremental Harvest Model." Procedia Engineering 29 (2012): 1081–87. http://dx.doi.org/10.1016/j.proeng.2012.01.093.

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17

Kadri, Salim, Sofiane Aouag, and Djalal Hedjazi. "An Incremental Model Projection Applied to Streamline Software Architecture Assessment and Monitoring." International Journal of Information System Modeling and Design 12, no. 3 (July 2021): 27–43. http://dx.doi.org/10.4018/ijismd.2021070102.

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Managing software architecture represents a big challenge throughout the development lifecycle. The complexity of the involved structural elements and the relations among them make the specified models look oversized and fuzzy, which makes the architecture incomprehensible, hard to maintain, and difficult to assess its quality. This paper's goal is to propose a powerful methodology for simplifying and reducing models' complexity to increase understandability, smoothing maintenance, and facilitating architecture monitoring and assessment. For this purpose, the authors rely heavily on two major concepts, multi-view modeling, and incremental model projection. The multi-viewing requires that all models must have two main views to describe the architecture and the mapping to its relevant quality attributes. The incremental projection is a methodology used to specialize and minimize models progressively to make them simpler and clearer. The results show that projecting models incrementally can reduce and narrow them significantly.

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18

WON, WOONG-JAE, JIYOUNG YEO, SANG-WOO BAN, and MINHO LEE. "BIOLOGICALLY MOTIVATED INCREMENTAL OBJECT PERCEPTION BASED ON SELECTIVE ATTENTION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 08 (December 2007): 1293–305. http://dx.doi.org/10.1142/s021800140700596x.

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In this paper, we propose an object selective attention and perception system, which was implemented by integrating a specific object preferable attention model with an incremental object perception model. The object oriented attention model can selectively pay attention to the candidates of an object in natural scenes based on a bottom-up selective attention model in conjunction with a top-down biased attention mechanism for a specific object. A generative model based on an incremental Bayesian parameter estimation is considered in order to perceive arbitrary objects in the attended areas. Combining an object oriented attention model with general object perception model, the developed system cannot only pay attention to a specific target object but can also memorize the characteristics of task nonspecific objects in an incremental manner. Experimental results show that the developed system generates good performance in successfully focusing on the target objects as well as incrementally perceiving objects in natural scenes.

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19

Lynch, Kristine E., Stephen A. Deppen, Scott L. DuVall, Benjamin Viernes, Aize Cao, Daniel Park, Elizabeth Hanchrow, Kushan Hewa, Peter Greaves, and Michael E. Matheny. "Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data Model: A Multidimensional Quality Assurance Approach." Applied Clinical Informatics 10, no. 05 (October 2019): 794–803. http://dx.doi.org/10.1055/s-0039-1697598.

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Abstract Background The development and adoption of health care common data models (CDMs) has addressed some of the logistical challenges of performing research on data generated from disparate health care systems by standardizing data representations and leveraging standardized terminology to express clinical information consistently. However, transforming a data system into a CDM is not a trivial task, and maintaining an operational, enterprise capable CDM that is incrementally updated within a data warehouse is challenging. Objectives To develop a quality assurance (QA) process and code base to accompany our incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse health care database into the Observational Medical Outcomes Partnership (OMOP) CDM to prevent incremental load errors. Methods We designed and implemented a multistage QA) approach centered on completeness, value conformance, and relational conformance data-quality elements. For each element we describe key incremental load challenges, our extract, transform, and load (ETL) solution of data to overcome those challenges, and potential impacts of incremental load failure. Results Completeness and value conformance data-quality elements are most affected by incremental changes to the CDW, while updates to source identifiers impact relational conformance. ETL failures surrounding these elements lead to incomplete and inaccurate capture of clinical concepts as well as data fragmentation across patients, providers, and locations. Conclusion Development of robust QA processes supporting accurate transformation of OMOP and other CDMs from source data is still in evolution, and opportunities exist to extend the existing QA framework and tools used for incremental ETL QA processes.

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20

Sun, Jie, Hui Li, Pei-Chann Chang, and Qing-Hua Huang. "Dynamic credit scoring using B & B with incremental-SVM-ensemble." Kybernetes 44, no. 4 (April 7, 2015): 518–35. http://dx.doi.org/10.1108/k-02-2014-0036.

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Purpose – Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring. Design/methodology/approach – This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring. Findings – The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model. Originality/value – This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

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21

Li, Jiuhua, Xiaobing Dang, Kai He, Qiyang Zuo, and Ruxu Du. "A NEW TYPE OF INCREMENTAL BENDING METHOD FOR COMPLICATED CURVED SHEET METAL." Transactions of the Canadian Society for Mechanical Engineering 40, no. 4 (November 2016): 433–43. http://dx.doi.org/10.1139/tcsme-2016-0032.

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A new type of incremental bending process for complicated curved sheet metal is proposed in the paper. The blank sheet is bended incrementally step by step. To validate the forming process, an incremental bending prototype is designed and manufactured, which was composed of a 3-DOF working table, a flexible supporting system and a 3D scanning system. The forming trajectory based on the theory of the minimum energy is planned according to the designed model of the sheet metal. Several experiments are carried out and the designed part is manufactured, which validated the proposed incremental forming method was successful.

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22

Pretschner, Alexander, Heiko Lötzbeyer, and Jan Philipps. "Model based testing in incremental system development." Journal of Systems and Software 70, no. 3 (March 2004): 315–29. http://dx.doi.org/10.1016/s0164-1212(03)00076-1.

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23

Zakhama, N., and J. A. De La Puente. "A target code model for incremental prototyping." IFAC Proceedings Volumes 27, no. 15 (September 1994): 101–6. http://dx.doi.org/10.1016/s1474-6670(17)45758-2.

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24

Sung, Jaewon, and Daijin Kim. "Adaptive active appearance model with incremental learning." Pattern Recognition Letters 30, no. 4 (March 2009): 359–67. http://dx.doi.org/10.1016/j.patrec.2008.11.006.

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25

Kim, Byung Joo. "Incremental Eigenspace Model Applied to Monitoring System." International Journal of Security and Its Applications 8, no. 5 (September 30, 2014): 243–52. http://dx.doi.org/10.14257/ijsia.2014.8.5.22.

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26

YANO, TOKUO. "Physiological model of CO2output during incremental exercise." Ergonomics 40, no. 5 (May 1997): 522–30. http://dx.doi.org/10.1080/001401397188008.

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27

Sminchisescu, C., D. Metaxas, and S. Dickinson. "Incremental model-based estimation using geometric constraints." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 5 (May 2005): 727–38. http://dx.doi.org/10.1109/tpami.2005.104.

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28

Prozzi, Jorge A., and Samer M. Madanat. "Incremental Nonlinear Model for Predicting Pavement Serviceability." Journal of Transportation Engineering 129, no. 6 (November 2003): 635–41. http://dx.doi.org/10.1061/(asce)0733-947x(2003)129:6(635).

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29

Pinto, Rafael Coimbra, and Paulo Martins Engel. "Correction: A Fast Incremental Gaussian Mixture Model." PLOS ONE 10, no. 10 (October 28, 2015): e0141942. http://dx.doi.org/10.1371/journal.pone.0141942.

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30

Chang, W. T., and S. A. Lin. "Incremental maneuver estimation model for target tracking." IEEE Transactions on Aerospace and Electronic Systems 28, no. 2 (April 1992): 439–52. http://dx.doi.org/10.1109/7.144570.

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31

Ramadevi, L. "Automatic Learning Image Objects via Incremental Model." IOSR Journal of Computer Engineering 14, no. 3 (2013): 70–78. http://dx.doi.org/10.9790/0661-1437078.

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32

Kohn, Wolf, Philip C. Placek, Zelda B. Zabinsky, and Jonathan Cross. "An Incremental Probability Model for Dynamic Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 6 (June 2020): 2083–92. http://dx.doi.org/10.1109/tsmc.2018.2797119.

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33

Liu, Qiang, Xiaoshe Dong, Heng Chen, and Yinfeng Wang. "IncPregel: an incremental graph parallel computation model." Frontiers of Computer Science 12, no. 6 (December 2018): 1076–89. http://dx.doi.org/10.1007/s11704-016-6109-y.

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34

Chen, Z. M., S. M. Gao, and W. D. Li. "An approach to incremental feature model conversion." International Journal of Advanced Manufacturing Technology 32, no. 1-2 (January 27, 2006): 99–108. http://dx.doi.org/10.1007/s00170-005-0316-2.

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35

Schrammel, Peter, Daniel Kroening, Martin Brain, Ruben Martins, Tino Teige, and Tom Bienmüller. "Incremental bounded model checking for embedded software." Formal Aspects of Computing 29, no. 5 (February 22, 2017): 911–31. http://dx.doi.org/10.1007/s00165-017-0419-1.

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36

Mehler, Tilman, and Stefan Edelkamp. "Dynamic Incremental Hashing in Program Model Checking." Electronic Notes in Theoretical Computer Science 149, no. 2 (February 2006): 51–69. http://dx.doi.org/10.1016/j.entcs.2005.07.026.

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37

Li, Beibei, Steven M. Roper, Lei Wang, Xiaoyu Luo, and N. A. Hill. "An incremental deformation model of arterial dissection." Journal of Mathematical Biology 78, no. 5 (November 19, 2018): 1277–98. http://dx.doi.org/10.1007/s00285-018-1309-8.

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38

Singh, Amninder. "A SIMULATION MODEL FOR INCREMENTAL SOFTWARE DEVELOPMENT LIFE CYCLE MODEL." International Journal of Advanced Research in Computer Science 8, no. 7 (August 20, 2017): 126–32. http://dx.doi.org/10.26483/ijarcs.v8i7.4136.

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39

MA, HUI, YONG TANG, and LINGKUN WU. "INCREMENTAL MINING OF PROCESSES WITH LOOPS." International Journal on Artificial Intelligence Tools 20, no. 01 (February 2011): 221–35. http://dx.doi.org/10.1142/s0218213011000103.

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Currently most researches in process mining focus on discovering a workflow model from an entire log. In fact, the process designers may have a partially built model and their prior knowledge is also valuable information to process mining. Besides, the large volume of log data makes the process mining a time-consuming job. It is better that the process mining be done in an incremental way. There are only a few methods that can incrementally mine a model, but they have their limitations such as incapability in handling loops or being intolerant to noise. On the other hand, loop mining is a challenging problem in process mining because the repeatedly executed tasks add complexity to the search for task precedence. This paper studies the problem of handling loops in process mining and proposes an improved incremental process mining method which supports loops. Experiments in the end show the feasibility and validity of the proposed method.

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Wong, Derek F., Lidia S. Chao, and Xiaodong Zeng. "iSentenizer-μ: Multilingual Sentence Boundary Detection Model." Scientific World Journal 2014 (April 15, 2014): 1–10. http://dx.doi.org/10.1155/2014/196574.

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Sentence boundary detection (SBD) system is normally quite sensitive to genres of data that the system is trained on. The genres of data are often referred to the shifts of text topics and new languages domains. Although new detection models can be retrained for different languages or new text genres, previous model has to be thrown away and the creation process has to be restarted from scratch. In this paper, we present a multilingual sentence boundary detection system (iSentenizer-μ) for Danish, German, English, Spanish, Dutch, French, Italian, Portuguese, Greek, Finnish, and Swedish languages. The proposed system is able to detect the sentence boundaries of a mixture of different text genres and languages with high accuracy. We employ i+Learning algorithm, an incremental tree learning architecture, for constructing the system. iSentenizer-μ, under the incremental learning framework, is adaptable to text of different topics and Roman-alphabet languages, by merging new data into existing model to learn the new knowledge incrementally by revision instead of retraining. The system has been extensively evaluated on different languages and text genres and has been compared against two state-of-the-art SBD systems, Punkt and MaxEnt. The experimental results show that the proposed system outperforms the other systems on all datasets.

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Madhusudhanan, Sathya, Suresh Jaganathan, and Jayashree L S. "Incremental Learning for Classification of Unstructured Data Using Extreme Learning Machine." Algorithms 11, no. 10 (October 17, 2018): 158. http://dx.doi.org/10.3390/a11100158.

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Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.

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42

Jehn, Florian U., Lutz Breuer, Tobias Houska, Konrad Bestian, and Philipp Kraft. "Incremental model breakdown to assess the multi-hypotheses problem." Hydrology and Earth System Sciences 22, no. 8 (August 29, 2018): 4565–81. http://dx.doi.org/10.5194/hess-22-4565-2018.

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Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

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43

CHI, Guo-tai, Feng CHI, and Guang-jun ZHAO. "Optimization Model of Incremental Loan Portfolio based on Risks Overlap of Incremental and Existing Portfolio." Systems Engineering - Theory & Practice 29, no. 4 (April 2009): 1–18. http://dx.doi.org/10.1016/s1874-8651(10)60015-4.

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44

Yeom, Chan-Uk, and Keun-Chang Kwak. "Incremental Granular Model Improvement Using Particle Swarm Optimization." Symmetry 11, no. 3 (March 18, 2019): 390. http://dx.doi.org/10.3390/sym11030390.

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This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.

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45

Hölscher, Paul. "An incremental model for cyclic compaction of sand." Soil Dynamics and Earthquake Engineering 105 (February 2018): 27–36. http://dx.doi.org/10.1016/j.soildyn.2017.11.010.

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46

Wang, JianGuo, Joshua Zhexue Huang, Dingming Wu, Jiafeng Guo, and Yanyan Lan. "An incremental model on search engine query recommendation." Neurocomputing 218 (December 2016): 423–31. http://dx.doi.org/10.1016/j.neucom.2016.09.003.

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47

Bai, Xiao, Peng Ren, Huigang Zhang, and Jun Zhou. "An incremental structured part model for object recognition." Neurocomputing 154 (April 2015): 189–99. http://dx.doi.org/10.1016/j.neucom.2014.12.004.

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48

Zhang, Hancui, Shuyu Chen, Jun Liu, Zhen Zhou, and Tianshu Wu. "An incremental anomaly detection model for virtual machines." PLOS ONE 12, no. 11 (November 8, 2017): e0187488. http://dx.doi.org/10.1371/journal.pone.0187488.

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49

Hapfelmeier, Andreas, Bernhard Pfahringer, and Stefan Kramer. "Pruning Incremental Linear Model Trees with Approximate Lookahead." IEEE Transactions on Knowledge and Data Engineering 26, no. 8 (August 2014): 2072–76. http://dx.doi.org/10.1109/tkde.2013.132.

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50

孙, 树平. "ECG Classification Based on Incremental Gaussian Mixture Model." Modeling and Simulation 09, no. 02 (2020): 105–15. http://dx.doi.org/10.12677/mos.2020.92012.

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