Tree Models
Diagnosis of Breast Cancer using Decision Tree Models and SVM
... decision tree modeling algorithms and compared with one that achieved by radial basis function kernel support vector machine (RBF- SVM) on the diagnosis of breast cancer using cytological proven tumor ...Four ...
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Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification
... decision-tree models, as the best representative of the entire big ...decision tree and to the most accurate tree on a validation ...decision tree is more computationally efficient than ...
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The geometry of independence tree models with hidden variables
... independence models on trees stating the result of Allman and Rhodes [1] on equa- tions defining the ...Markov models in a more efficient ...Markov models in the case of trivalent trees in terms of ...
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Semi-Supervised Induction of POS-Tag Lexicons with Tree Models
... two models for this task: a character-based recurrent neural network, which guesses the tag from the string form of the word, and a recently proposed graph-based model of morpho- logical ...Markov Tree ...
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Decision Tree Models Applied to the Labeling of Text with Parts of Speech
... Decision Tree Models Applied to the Labeling of Text with Parts of Speech D e c i s i o n Tree M o d e l s A p p l i e d to t h e L a b e l i n g o f T e x t w i t h P a r t s o f S p e e c h Ezra Bla[.] ...
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An Analysis of Decision Tree Models for Diabetes
... decision tree models for classification using LAD tree, NB tree and a Genetic J48 ...decision tree based classifier models study includes various parameters like computational ...
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Can plasticity make spatial structure irrelevant in individual-tree models?
... individual-tree models predict growth and mor- tality rates as functions of the target tree size, and of a competition or resource capture index that encapsulates neighbourhood ...aspatial ...
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Asymptotic model selection and identifiability of directed tree models with hidden variables
... graphical models induced by trees such that all the variables in the system are binary and in addition we do not observe the inner ...These models in general do not allow us to use the standard asymptotic ...
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Automatic Derivation of Fault Tree Models from SysML Models for Safety Analysis
... A methodology is introduced in [13] to perform SA on SysML models by us- ing Python, a generic programming language. The SA techniques used are FMEA and FTA. The system functional architecture is modeled using a ...
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Hidden Markov tree models for semantic class induction
... This method is similar to the one proposed by Pal et al. (2006). The advantage of the k -best projec- tion is that we control the complexity of the op- erations, but not the error, while the advantage of the ε-best ...
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Statistical Decision Tree Models for Parsing
... Evaluating SPATTER against the Penn Treebank Wall Street Journal corpus using the PARSEVAL measures, SPATTER achieves 86% precision, 86% recall, and 1.3 crossing brackets per sentence fo[r] ...
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Learning Latent Tree Graphical Models
... of models that has received considerable attention in the literature is the class of latent tree models, that is, graphical models Markov on trees, in which variables at some nodes represent ...
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Influence of Part of Speech and Phrasal Category Universal Tag set in Tree to Tree Translation Models
... This paper presents the application of universal tag-set defined at the POS and the phrasal category level to tree-to-tree models. A phrasal category tag-set is defined for Chinese, English, French, ...
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Runtime Optimizations for Tree-Based Machine Learning Models
... mize tree execution is not novel, there are a few minor differences between our implementation and those from previous work that may have substantial impact on per- ...of tree models by Sharp [22] ...
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Joint Decoding of Tree Transduction Models for Sentence Compression
... erative models and discriminative decision tree ...compression models (McDonald, 2006) are capable of inte- grating rich features and have been proved effec- tive for this ...
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Structured Prediction Models via the Matrix Tree Theorem
... Matrix- Tree Theorem (Tutte, ...log-linear models and max-margin ...max-margin models, Bartlett et ...spanning-tree models (McDon- ald et ...
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Learning Multi-Tree Classification Models with Ant Colony Optimization
... multi- tree models, is that it did not performed, overall, bet- ter than ...decision tree induction ...internal tree node, and its at- tribute values become branches to split the training set ...
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Tree Branch Level of Detail Models for Forest Navigation
... Texture-lobe models are a recent approach for representing foliage envelopes ...divide tree models into visually important and less important parts; a tree is represented by a cluster of lobes ...
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Large-deviation analysis and applications Of learning tree-structured graphical models
... Graphical models, which provide parsimonious representations for multivariate prob- ability distributions, grew out of the artificial intelligence community [153] and form a popular area of research in the machine ...
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Adapting Topic Models using Lexical Associations with Tree Priors
... topic models, people often care about inter- pretability, which can be approximated us- ing measures of lexical ...using tree priors, which provide a flexible framework that can take advan- tage of both ...
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