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Academic literature on the topic 'Classification tree models'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Classification tree models"

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Verbyla, David L. "Classification trees: a new discrimination tool." Canadian Journal of Forest Research 17, no. 9 (1987): 1150–52. http://dx.doi.org/10.1139/x87-177.

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Classification trees are discriminant models structured as dichtomous keys. A simple classification tree is presented and contrasted with a linear discriminant function. Classification trees have several advantages when compared with linear discriminant analysis. The method is robust with respect to outlier cases. It is nonparametric and can use nominal, ordinal, interval, and ratio scaled predictor variables. Cross-validation is used during tree development to prevent overrating the tree with too many predictor variables. Missing values are handled by using surrogate splits based on nonmissin

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Diligenti, M., P. Frasconi, and M. Gori. "Hidden tree markov models for document image classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 4 (2003): 520–24. http://dx.doi.org/10.1109/tpami.2003.1190578.

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Povkhan, I. F. "THE METHOD OF BOUNDED CONSTRUCTIONS OF LOGICAL CLASSIFICATION TREES IN THE PROBLEM OF DISCRETE OBJECTS CLASSIFICATION." Ukrainian Journal of Information Technology 3, no. 1 (2021): 22–29. http://dx.doi.org/10.23939/ujit2021.03.022.

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The problem of constructing a model of logical classification trees based on a limited method of selecting elementary features for geological data arrays is considered. A method for approximating an array of real data with a set of elementary features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree is proposed. This approach allows to ensure the necessary accuracy of the model, reduce its structural complexity, and achieve the necessary performance indicators. A limited method for constructing classification trees has been develope

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Maschler, Julia, Clement Atzberger, and Markus Immitzer. "Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data." Remote Sensing 10, no. 8 (2018): 1218. http://dx.doi.org/10.3390/rs10081218.

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Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of

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Thoe, Wai, King Wah Choi, and Joseph Hun-wei Lee. "Predicting ‘very poor’ beach water quality gradings using classification tree." Journal of Water and Health 14, no. 1 (2015): 97–108. http://dx.doi.org/10.2166/wh.2015.094.

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A beach water quality prediction system has been developed in Hong Kong using multiple linear regression (MLR) models. However, linear models are found to be weak at capturing the infrequent ‘very poor’ water quality occasions when Escherichia coli (E. coli) concentration exceeds 610 counts/100 mL. This study uses a classification tree to increase the accuracy in predicting the ‘very poor’ water quality events at three Hong Kong beaches affected either by non-point source or point source pollution. Binary-output classification trees (to predict whether E. coli concentration exceeds 610 counts/

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Povkhan, Igor. "FEATURES OF SOFTWARE SOLUTIONS OF MODELS OF LOGICAL CLASSIFICATION TREES BASED ON SELECTION OF SETS OF ELEMENTARY FEATURES." Technical Sciences and Technologies, no. 4(22) (2020): 72–90. http://dx.doi.org/10.25140/2411-5363-2020-4(22)-72-90.

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Urgency of the research.Currently there are several independent approaches (concepts) to solve the classification problem in the general setting, and the development of various concepts, approaches, methods, and models that cover the general issues of the theory of artificial intelligence and information systems, all of these approaches in a recognition theory have their advantages and disadvantages and form a single tool to solve applied problems of the theory of artificial intelligence. This study will focus on the current concept of decision trees (classification trees). The general problem

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Khoshgoftaar, Taghi M., and Naeem Seliya. "Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm." International Journal on Artificial Intelligence Tools 12, no. 03 (2003): 207–25. http://dx.doi.org/10.1142/s0218213003001204.

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Predicting the quality of system modules prior to software testing and operations can benefit the software development team. Such a timely reliability estimation can be used to direct cost-effective quality improvement efforts to the high-risk modules. Tree-based software quality classification models based on software metrics are used to predict whether a software module is fault-prone or not fault-prone. They are white box quality estimation models with good accuracy, and are simple and easy to interpret. An in-depth study of calibrating classification trees for software quality estimation u

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Hu, Ruo, and Zan Fu Xie. "Classification of Knowledge Discovery Methods." Applied Mechanics and Materials 63-64 (June 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.859.

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Knowledge Discovery, the science and technology of exploring knowledge in order to discover previously unknown patterns, is a part of the overall process of getting information in databases. In today’s computer-driven world, these databases contain a lot of information. The significant value of this information makes knowledge discovery a matter of considerable importance and necessity. A decision tree is a predictive model which can be used to represent both classifiers and regression models. When a decision tree is used for classification tasks, it is more appropriately referred to as a clas

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Thakkar, Pooja. "Drug Classification using Black-box models and Interpretability." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1518–29. http://dx.doi.org/10.22214/ijraset.2021.38203.

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Abstract: The focus of this study is on drug categorization utilising Machine Learning models, as well as interpretability utilizing LIME and SHAP to get a thorough understanding of the ML models. To do this, the researchers used machine learning models such as random forest, decision tree, and logistic regression to classify drugs. Then, using LIME and SHAP, they determined if these models were interpretable, which allowed them to better understand their results. It may be stated at the conclusion of this paper that LIME and SHAP can be utilised to get insight into a Machine Learning model an

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Lim, Chee Soon, Edy Tonnizam Mohamad, Mohammad Reza Motahari, Danial Jahed Armaghani, and Rosli Saad. "Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study." Applied Sciences 10, no. 17 (2020): 5734. http://dx.doi.org/10.3390/app10175734.

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To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiase

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Dissertations / Theses on the topic "Classification tree models"

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Liu, Dan. "Tree-based Models for Longitudinal Data." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1399972118.

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Keller-Schmidt, Stephanie. "Stochastic Tree Models for Macroevolution." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-96504.

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Phylogenetic trees capture the relationships between species and can be investigated by morphological and/or molecular data. When focusing on macroevolution, one considers the large-scale history of life with evolutionary changes affecting a single species of the entire clade leading to the enormous diversity of species obtained today. One major problem of biology is the explanation of this biodiversity. Therefore, one may ask which kind of macroevolutionary processes have given rise to observable tree shapes or patterns of species distribution which refers to the appearance of branching orde

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Shafi, Ghufran. "Development of roadway link screening criteria for microscale carbon monoxide and particulate matter conformity analyses through application of classification tree model." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28222.

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Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Guensler, Randall; Committee Member: Rodgers, Michael; Committee Member: Russell, Armistead.

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Victors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.

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Hidden Markov Models (HMMs) have seen widespread use in a variety of applications ranging from speech recognition to gene prediction. While developed over forty years ago, they remain a standard tool for sequential data analysis. More recently, Latent Dirichlet Allocation (LDA) was developed and soon gained widespread popularity as a powerful topic analysis tool for text corpora. We thoroughly develop LDA and a generalization of HMMs and demonstrate the conjunctive use of both methods in predictive data analysis for health care problems. While these two tools (LDA and HMM) have been used in co

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Shew, Cameron Hunter. "TRANSFERABILITY AND ROBUSTNESS OF PREDICTIVE MODELS TO PROACTIVELY ASSESS REAL-TIME FREEWAY CRASH RISK." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/863.

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This thesis describes the development and evaluation of real-time crash risk assessment models for four freeway corridors, US-101 NB (northbound) and SB (southbound) as well as I-880 NB and SB. Crash data for these freeway segments for the 16-month period from January 2010 through April 2011 are used to link historical crash occurrences with real-time traffic patterns observed through loop detector data. The analysis techniques adopted for this study are logistic regression and classification trees, which are one of the most common data mining tools. The crash risk assessment models are develo

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Motloung, Rethabile Frangenie. "Understanding current and potential distribution of Australian acacia species in southern Africa." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79720.

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This dissertation presents research on the value of using different sources of data to explore the factors determining invasiveness of introduced species. The research draws upon the availability of data on the historical trial plantings of alien species and other sources. The focus of the study is on Australian Acacia species as a taxon introduced into southern Africa (Lesotho, South Africa and Swaziland). The first component of the study focused on understanding the factors determining introduction outcome of species in historical trial plantings and invasion success of Australian Acac

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Mugodo, James, and n/a. "Plant species rarity and data restriction influence the prediction success of species distribution models." University of Canberra. Resource, Environmental & Heritage Sciences, 2002. http://erl.canberra.edu.au./public/adt-AUC20050530.112801.

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There is a growing need for accurate distribution data for both common and rare plant species for conservation planning and ecological research purposes. A database of more than 500 observations for nine tree species with different ecological and geographical distributions and a range of frequencies of occurrence in south-eastern New South Wales (Australia) was used to compare the predictive performance of logistic regression models, generalised additive models (GAMs) and classification tree models (CTMs) using different data restriction regimes and several model-building strategies. Environme

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Lazaridès, Ariane. "Classification trees for acoustic models : variations on a theme." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0016/MQ37139.pdf.

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Löwe, Rakel, and Ida Schneider. "Automatic Differential Diagnosis Model of Patients with Parkinsonian Syndrome : A model using multiple linear regression and classification tree learning." Thesis, Uppsala universitet, Tillämpad kärnfysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413638.

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Parkinsonian syndrome is an umbrella term including several diseases with similar symptoms. PET images are key when differential diagnosing patients with parkinsonsian syndrome. In this work two automatic diagnosing models are developed and evaluated, with PET images as input, and a diagnosis as output. The two devoloped models are evaluated based on performance, in terms of sensitivity, specificity and misclassification error. The models consists of 1) regression model and 2) either a decision tree or a random forest. Two coefficients, alpha and beta, are introduced to train and test the mode

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Purcell, Terence S. "The use of classification trees to characterize the attrition process for Army manpower models." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA336747.

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Books on the topic "Classification tree models"

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Purcell, Terence S. The use of classification trees to characterize the attrition process for Army manpower models. Naval Postgraduate School, 1997.

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The Use of Classification Trees to Characterize the Attrition Process for Army Manpower Models. Storming Media, 1997.

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Shorter, Edward, and Max Fink. Karl Kahlbaum. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190881191.003.0003.

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In 1874, Karl Kahlbaum, a German psychiatrist in an obscure private hospital, pulled various symptom pictures together into a single diagnosis: “catatonia.” Kahlbaum had earlier pioneered the modern classification of illness with his concepts of course and outcome as demarcating the various disease entities. He thought that, similar to neurosyphilis, catatonia had a common cause and common clinical course but, unlike neurosyphilis, often a relatively benign outcome. He believed the illness progressed in fixed stages. At the same time, Kahlbaum’s associate, Ewald Hecker, described madness in yo

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Book chapters on the topic "Classification tree models"

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Margineantu, Dragos D., and Thomas G. Dietterich. "Improved Class Probability Estimates from Decision Tree Models." In Nonlinear Estimation and Classification. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_10.

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Suthaharan, Shan. "Decision Tree Learning." In Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_10.

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Suthaharan, Shan. "Chandelier Decision Tree." In Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_13.

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Cappelli, Carmela, Francesco Mola, and Roberta Siciliano. "Selecting Regression Tree Models: a Statistical Testing Procedure1." In Advances in Classification and Data Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59471-7_31.

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Gatnar, Eugeniusz. "Tree-based Models in Statistics: Three Decades of Research." In Classification, Clustering, and Data Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_44.

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Ramírez-Corona, Mallinali, L. Enrique Sucar, and Eduardo F. Morales. "Multi-label Classification for Tree and Directed Acyclic Graphs Hierarchies." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_27.

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Fiasché, Maurizio. "SVM Tree for Personalized Transductive Learning in Bioinformatics Classification Problems." In Recent Advances of Neural Network Models and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04129-2_22.

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Liu, Yi, and Taghi M. Khoshgoftaar. "Building Decision Tree Software Quality Classification Models Using Genetic Programming." In Genetic and Evolutionary Computation — GECCO 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45110-2_75.

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Dutt, Rohit, Harish Dureja, and A. K. Madan. "Classification Models Using Decision Tree, Random Forest, and Moving Average Analysis." In New Frontiers in Nanochemistry. Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429022951-6.

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Gal-Or, Mordechai, Jerrold H. May, and William E. Spangler. "Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_19.

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Conference papers on the topic "Classification tree models"

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Gupta, Mahendra, and S. Minz. "Spatial data classification using decision tree models." In 2017 Conference on Information and Communication Technology (CICT). IEEE, 2017. http://dx.doi.org/10.1109/infocomtech.2017.8340605.

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Salama, Khalid M., and Fernando E. B. Otero. "Learning Multi-tree Classification Models with Ant Colony Optimization." In International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005071300380048.

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Romberg, J., Hyeokho Choi, R. Baraniuk, and N. Kingbury. "Multiscale classification using complex wavelets and hidden Markov tree models." In Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899396.

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Yasser, Khaled, and Elsayed Hemayed. "Location category classification using tree based models with novelty discrimination." In 2017 13th International Computer Engineering Conference (ICENCO). IEEE, 2017. http://dx.doi.org/10.1109/icenco.2017.8289799.

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Zhuowen Tu. "Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.194.

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"Using a Fuzzy Decision Tree Ensemble for Tumor Classification from Gene Expression Data." In Special Session on Computational Models based on Soft Computing and its Applications. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004658203200331.

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Wang, Zhenghai, Guangdao Hu, YongZhang Zhou, and Xin Liu. "A classification model of Hyperion image base on SAM combined decision tree." In Geoinformatics 2008 and Joint Conference on GIS and Built environment: Advanced Spatial Data Models and Analyses, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2009. http://dx.doi.org/10.1117/12.813161.

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Larasati, Aisyah, Muhammad Farhan, Puji Rahmawati, Nabila Azzahra, Apif Miftahul Hajji, and Anik Nur Handayani. "Designing Classification Models of Patron Visits to an Academic Library using Decision Tree." In Proceedings of the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/icoemis-19.2019.20.

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To, Cuong, Tuan D. Pham, Tuan Pham, and Xiaobo Zhou. "Binary Classification using Decision Tree based Genetic Programming and Its Application to Analysis of Bio-mass Data." In 2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-09). AIP, 2010. http://dx.doi.org/10.1063/1.3314262.

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Fadaie, Gholamreza. "The Influence of Classification on World View and Epistemology." In InSITE 2008: Informing Science + IT Education Conference. Informing Science Institute, 2008. http://dx.doi.org/10.28945/3279.

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Worldview as a kind of man's look towards the world of reality has a severe influence on his classification of knowledge. In other words one may see in classification of knowledge the unity as well as plurality. This article deals with the fact that how classification takes place in man's epistemological process. Perception and epistemology are mentioned as the key points here. Philosophers are usually classifiers and their point of views forms the way they classify things and concepts. Relationship and how one looks at it in shaping the classification scheme is critical. The classifications w

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Reports on the topic "Classification tree models"

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Asher, Sam, Denis Nekipelov, Paul Novosad, and Stephen Ryan. Classification Trees for Heterogeneous Moment-Based Models. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22976.

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