Classification models
Creating classification models from textual descriptions of companies using crunchbase
... different models for multilabel text classification, using information collected from Crunchbase, a large data- base that holds information about more than 600000 ...proposed models predict the categories ...
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J. Braz. Chem. Soc. vol.24 número3
... supervised classification models (partial least-squares discriminant analysis (PLS-DA)), which were able to discriminate cocaine samples as a function of their content and chemical ...
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Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area
... main classification models applied for DSM (ANNs, logistic regression, and decision ...several models run in R software became popular in research dealing with soil ...used models for mapping ...
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A Reminiscence of ”Mastermind”: Iris/Periocular Biometrics by ”In-Set” CNN Iterative Analysis
... Abstract—Convolutional neural networks (CNNs) have emerged as the most popular classification models in biomet- rics research. Under the discriminative paradigm of pattern recognition, CNNs are used ...
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Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of Alzheimer's disease.
... built classification models utilizing different available datasets containing gene expression from two different regions: the cortex and the ...generated models using several different sets of genes, ...
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Improving the drug discovery process by using multiple classifier systems
... protocol, classification models described in Table 2 were optimized (with hyper-parameter configuration) using a straightforward 10-fold cross-validation strategy applied over the whole features set ...
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Machine learning for biomedical literature triage.
... The dataset instances were then expressed by means of mycoMINE annotation content, their corresponding bio-entities and EC numbers. Bioentity annotations are grouped according to their span. We considered two different ...
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Overview Of Streaming-Data Algorithms
... Mining time series data and data streams have been recognized as two of the top ten research problems in data mining. Among all data mining techniques, clustering can be used as the first step in the data mining process. ...
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Acoustic Models for the Automatic Identification of Prosodic Boundaries in Spontaneous Speech / Modelos acústicos para a identificação automática de fronteiras prosódicas na fala espontânea
... two classification methods: Random Forest (RF) and Linear Discriminant Analysis ...statistical classification were used to obtain hierarchical classification models based on the observation of ...
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Classification and quantification of defective and non-defective coffees by FTIR and NIR spectroscopy
... while classification models for ATR-FTIR and NIR were developed by Elastic ...correct classification, up to 100%, were achieved with each of the techniques ...correct classification of the ...
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Decimal Classification Editions
... Decimal Classification” was published in 2003, and the first edition of Electronic Dewey (ED), version ...Decimal Classification (1989) into the digital world, in which there were operated the changes ...
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Classification of foundry clients using business rules approach
... The aim of this paper was to present a possible application of a rule-based approach in clients’ classification. The proposed engine operates in accordance with general BRM idea but at t he same time includes ...
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Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models.
... CART models modestly increased predictive performance of 21-day death/LT compared to traditional regression-derived KCC using United States ALFSG ...NEW-CART models provide further but marginal improvement ...
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Subject classification through context-enriched language models
... Our models focus on two different extra-textual elements: the degree of interest of a user towards a subject (Agent Knowledge) and the likelihood that an important event related to that subject is happening ...
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Multilevel model analysis using R
... multilevel models are unbiased, but have a larger sampling variance compared to OLS methods resulted ...structure models without considering this aspect could lead to ...
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Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
... 5 models to perform classification: decision tree, random forest, linear SVM, voting ensembles of the previous three classifiers and a multi-layer perceptron neural network ...for classification, and ...
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Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia
... Land use change models such as LCM provided an important spatio-temporal information of land use and land cover changes especially on urban areas. It also provide the possibility to understand the influence of ...
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Implicit acquisition of grammars with crossed and nested non-adjacent dependencies: investigating the push-down stack model
... Whatever the mechanisms might be, we have shown that subjects need more exposure to settle on a correct representation for non-adjacent compared with adjacent dependencies. Thus, a possible reason for reported ...
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TABLA 1 - Puntuación de la Clasificación
... Objetivo: las clasificaciones han cam- biado en el último medio siglo, la más usada desde la pasada década es la Clasificación AO. En 2004, Vaccaro et al. propusieron el Thoraco-Lumbar In- jury Classification and ...
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Practical guidelines for modelling post-entry spread in invasion ecology
... knowledge of a critical process is incomplete, it is prudent to be cautious, and to be wary of management imperatives derived from regression-based patterns. The method of multiple competing hypotheses (Chamberlin 1890, ...
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