[PDF] Top 20 On the interpretability of machine learning-based model for predicting hypertension
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On the interpretability of machine learning-based model for predicting hypertension
... split based on particular cutoff values and conditions in a tree shape where each record in the dataset belongs to only one subset, leaf ...trees, predicting the outcome of an instance is done by navigating ... See full document
32
Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
... largely based on the Nash– Sutcliffe efficiency (NSE; Nash and Sutcliffe, 1970) which can be a flawed performance metric in highly seasonal wa- tersheds (Schaefli and Gupta, 2007; Legates and McCabe ...on ... See full document
18
Software Fault Proneness Prediction Using Support Vector Machines
... Several empirical studies have been carried out to predict the fault proneness models such as [1, 2, 5, 7, 11, 12, 14, 16, 20, 21, 23, 27, 34, 35, 40, 44]. There is a need to empirically validate the machine ... See full document
6
Predicting software maintainability in object-oriented systems using ensemble techniques
... several machine learning models have been applied with variable results and no clear indication of which techniques are more ...major machine learning based approaches for ... See full document
5
The Assessment of Machine Learning Model Performance for Predicting Alluvial Deposits Distribution
... for predicting even uncensored ...predictions based on the projection of predictive attributes of geological and geographic data from models such as global circulation models ...distribution model is ... See full document
6
OPTIMISATION OF HIDDEN MARKOV MODEL FOR DISTRIBUTED DENIAL OF SERVICE ATTACK PREDICTION USING VARIATI ONAL BAYESIAN
... series, Machine Learning (Seng, et al, 2010; Zhang, et al, 2012; Satpute, et al, 2013), Markov Chain (Shin, et al, 2013), Hidden Markov Model (HMM) (Cheng, et al, 2012; Sendi, et al, 2012), ... See full document
11
Protein Secondary Structure Prediction using Pattern Recognition Neural Network
... of predicting their structure and thereafter their function has been christened the Holy Grail of structural ...Using machine learning and data mining process, we developed a pattern recognition ... See full document
6
Machine Learning Algorithms for Oil Price Prediction
... for predicting the diesel price in ...(RBF model, polynomial model, and linear model), and Linear ...rates based on the previous datasets on the data and prices as the feature list are ... See full document
6
Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next generation sequencing data
... We considered 315 different combinations of gene lists, phenotype simulations, and gene expression data values. In particular, linear SVM, radial SVM, and LR were applied to lists of the top 1, 5, or 10 causal genes ... See full document
5
MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement
... by machine- learning techniques, it is worth noting that pure machine- learning techniques cannot completely replace hydrologic ...mental model and machine-learning ... See full document
17
Machine Learning Approaches to Predicting Company Bankruptcy
... method based on decision ...the model with random ...tree-like model that graphs possible consequences) (Beriman, ...the model predicts well using the training dataset but performs ... See full document
11
Virtual machine scheduling strategy based on machine learning algorithms for load balancing
... virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data ...algorithm based on genetic algorithm (SVR_GA), k -means ... See full document
16
Human-level Moving Object Recognition from Traffic Video
... reinforcement learning takes form of external ...network learning efficiency, and falling to local ...network learning model based on deep learning, which learns some more useful ... See full document
14
Original Article A combination of tumor and molecular markers predicts a poor prognosis in lung adenocarcinoma
... a model for the joint prediction of molec- ular and tumor markers has never been pro- posed, and due to the bias of the sample distri- bution, in order to further study the synergy between these factors, we chose ... See full document
12
Surprisal and Interference Effects of Case Markers in Hindi Word Order
... Information density and surprisal are mathemat- ically equivalent and both quantify the contex- tual predictability of a linguistic unit. But sur- prisal is based on different theoretical assumptions about ... See full document
13
Influence of Learning Model and Initial Knowledge on the Ability of Mathematic Connection
... innovative learning model, but the selection of learning models must be tailored to the characteristics of students, materials and environmental conditions where the learning process is ...the ... See full document
7
Smart Stick for Blind using Machine Learning
... given model has supersonic device and water sensor hooked up ...The model takes voice input and offers out the voice output concerning a way to navigate from a specific ...victimisation machine ... See full document
7
Determination of Compressive Strength of Concrete by Statistical Learning Algorithms
... [6] H. Suetani, A. M. Ideta, and J. Morimoto, “Nonlinear structure of escape-times to falls for a passive dynamic walker on an irregular slope: Anomaly detection using multi-class support vector machine and latent ... See full document
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Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
... EACL 2014 introduced a short paper (4 page) format in addition to the usual long paper (8 page) format, which led to the highest total number of submissions of any EACL. We received 317 valid long paper submissions and ... See full document
26
Prediction of Parkinson Disease by Best Accuracy using Supervised Classification Machine Learning Approach
... A classifier that categorizes the data set by setting an optimal hyper plane between data. I chose this classifier as it is incredibly versatile in the number of different kernelling functions that can be applied and ... See full document
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