Ensemble Statistical and Machine Learning
An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce
... original Adaboost algorithm as it can only boost one weak learner. Although there is a similar approach to combine different weak learners: the Adaboost Dynamic algorithm [95], the combination is done within the Adaboost ...
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Active Learning for Multilingual Statistical Machine Translation
... Active learning is framed as an iterative learn- ing ...active learning: we assume we are given a small amount of parallel text and a large amount of monolingual source language text; us- ing these ...
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Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods
... Wu et al. [74] developed a novel hybrid method for improving the accuracy of the electricity load forecasting. The proposed method was including an advanced integration of ELM, ensemble empirical mode ...
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Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods
... an ensemble based incremental SVM for fault diagnosis of ...and statistical analysis of classifier were obtained with ensemble-based SVM and compared with traditional ...domain statistical ...
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Light Weight Attack Defence System using Machine Learning Ensemble Approach
... and Machine learning ensemble, and tried their execution on a lot of benchmark DARPA ...and Machine Learning Ensemble approach convey profoundly exact outcomes (more prominent ...
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Detecting Diabetes Mellitus using Machine Learning Ensemble
... six machine learning algorithms will focus on diagnosing ...for statistical computing and graphics, it has a huge collection of machine learning and data mining algorithms and it is a ...
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A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading
... in statistical analysis that perform regression, which consists of a set of statistical processes for estimating the relationships among variables [12], with the goal of predicting the exact stock price for ...
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Crop Production-Ensemble Machine Learning Model for Prediction
... of statistical data over a period of time is the time series ...Vector Machine (SVM) and Naive ...proposed ensemble model used to project the crop production over a period of ...This ensemble ...
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Application Of Ensemble Machine Learning Methods To Improve Effort Prediction Accuracy
... using machine learning techniques has been done for effort estimation and no single technique is ...superior. Machine learning techniques use statistical analysis on datasets and help ...
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Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm: Ensemble Approach
... of machine learning and statistical techniques that can used to predict diabetes ...known machine learning algorithm (Random Forest (RF), KNN, Naïve Bayes, and J48) classification ...
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A Machine Learning Approach for Intrusion Detection using Ensemble Technique A Survey
... a machine or software that monitors the traffic in a network and on detection of a malicious packet, informs the user or a specific acting unit which can take further action and avoid the malicious packet from ...
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Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
... A systematic framework to build ensembles with optimized weights (COWE) for regression problems was introduced in this paper. The goal is to minimize bias and variance by combining different base learners using a ...
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Ensemble feature learning of genomic data using support vector machine
... an ensemble SVM (ESVM) for gene selection that follows the ensemble and bagging concepts of random forest and adopts the backward elimination strategy which is the rationale of recursive feature elimination ...
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Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age
... that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and ef- fective support tool for pregnant women for ...powerful ...
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NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer
... an ensemble of multiple callers boosted by ML as a variant fil- tering strategy is that the classifiers do not rely on manually defined ...an ensemble of embedded tools, incorporating a novel algorithm that ...
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Active Learning for Statistical Phrase based Machine Translation
... Despite the promise of active learning for SMT for domain adaptation and low-density/low-resource languages, there has been very little work published on this issue. A Ph.D. proposal by Chris Callison- Burch ...
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Ensemble Triangulation for Statistical Machine Translation
... current statistical machine trans- lation (SMT) systems is to build cheap and rapid corpus-based SMT systems without involving hu- man translation ...
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Machine learning and statistical approaches to classification – a case study
... between states (ET). Changes in the price based on a moving average of the last 24 hours is used to evaluate the output and the assigned class (1 or -1) for the output is a reflection of the deviations of the price on a ...
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Statistical and Machine Learning Techniques for Prediction of Customer Churn in Telecom
... In order for our analysis to be valid, our model has to satisfy the assumptions of logistic regression. Therefore, before we use our model to make any statistical inference, we check for outliers in our data that ...
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Integrating Intrusion Detection Model Using Ensemble of Classifiers and Extreme Learning Machine
... Extreme learning machine algorithm can classify the original dataset on traffic related normal or abnormal and training time algorithm can predict ELM actual value and predict ...
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