machine learning data sets
A Framework for Implementing Machine Learning algorithms using Data sets
... organizing learning system, and the learning performance was the feedback adjustment of the transmission signal of the threshold logic ...concept-oriented learning, which is symbolic ...the ...
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IMPROVE THE ACCURACY OF CLASSIFIERS PERFORMANCE USING MACHINE LEARNING & DATA PREPROCESSED METHODS ON NSL-KDD DATA SETS.
... supervised learning methods, NSL KDD is the available dataset that provides labels for both training and test ...training data that include 7 weeks of network traffic in the form of Transmission Control ...
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On an attempt to explore challenges for Artificial Intelligence and Machine Learning in Indian Military and Defence Sector and Studying the Possible Inter relationship amongst them using ISM Methodology
... large data sets (DDS) ; Limited training data (LTD); Challenge of cyber security (CCS); Insufficient funds (IF) ; Large variability in network traffic (LVNT); Difficulty in labelling data ...
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Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods
... special data set which does not include the attacks which can be easily identified through learning ...test sets one with all difficulty level of attacks and another in which attacks which can be ...
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Implementing IoT and Data Analytics To Overcome "Vehicles Danger"
... the data sets till the voice assistant message. Data sets will be of pit holes and sewage, animals (dogs and cats) which guide Machine Learning to learn its task to perform ...
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Fall Detection and Activity Recognition with Machine Learning
... and machine learning algorithms to detect six common ...attribute sets in isolation and then in ...eight machine learning algorithms, from which Support Vector Machine produced ...
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Evaluating parameters for ligand-based modeling with random forest on sparse data sets
... growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust ...four data sets and studied the ...
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Traffic Data Analysis using Decision Tree and Naïve Bayes Classifier
... Irvine Machine Learning Repository is hosted by the Center for Machine Learning and Intelligent Systems at UC ...maintain data as a service to the machine learning ...in ...
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pomegranate: Fast and Flexible Probabilistic Modeling in Python
... source machine learning package for probabilistic modeling in ...from data sets as a strategy for training ...useful learning strategies, such as out-of-core learning, minibatch ...
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Learning Coordinate Covariances via Gradients
... of data sets with many variables or coordinates in the biological and physical sciences has driven the use of a variety of machine learning approaches based on Tikhonov regularization or ...
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SMART: An Open Source Data Labeling Platform for Supervised Learning
... supervised machine learning has made tremendous strides, achieving or surpassing human-level performance on tasks diverse as image recognition (Real et ...in machine learning adoption is no ...
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Data Mining Using Relational Database Management Systems
... of learning algorithms with rela- tional ...Weka data mining library, WekaDB, which allows the data used by the learning algorithms to reside on secondary ...of machine learning ...
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Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm: Ensemble Approach
... al.[7] machine learning algorithms are very important to predict different medical data sets including diabetes diseases ...Vector Machine ) algorithm provides best accuracy than the ...
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Machine Learning Processing for Intrusion Detection
... are Machine Learning (ML) Approaches. In this paper a machine learning approach is used to distinguish between normal and abnormal ...big data sets. Machine ...
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Improving nearest neighbor classifier using tabu search and ensemble distance metrics
... The nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is ...
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Encog: Library of Interchangeable Machine Learning Models for Java and C#
... platform machine learning framework that was first released in ...of machine learning models to be applied to data sets using regression, classification, and ...supported ...
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Sorting Texts by Readability
... been proposed (Klare 1963; DuBay 2004a, 2004b; Schwarm and Ostendorf 2005; Pitler and Nenkova 2008). In this work, we only utilize the most basic features of vocabulary in terms of word frequencies for three reasons. ...
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Survey on Artificial Intelligence in Healthcare
... effective learning and adaptation model, it provides solutions to several engineering ...for machine to adapt to the recent trend in the consumer ...a machine performs which otherwise would have been ...
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Title: STUDENT ACADEMIC PERFORMANCE USING DATA MINING TECHNIQUES
... Although many classification models exist, only some have been selected within the scope of this study. The selected algorithms are Naive Bayesian algorithm; MLP, SMO, J48, REP tree, RANDOM tree and Decision table are ...
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Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets
... sparse data sets depreciates their performance, results from pre-trained and re- trained RNTN are presented to further support this ...when learning word embeddings, resulting word embeddings are ...
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