Evaluation of the Supervised Learning Algorithms
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer Diagnosis
... machine learning and neural network ...capability. Supervised learning algorithms such as Feed-Forward Backpropagation, Cascade-Forward Backpropagation and Perceptron networks participated in ...
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Scalable real time parking lot classification: an evaluation of image features and supervised learning algorithms
... self- supervised learning algorithm that automatically obtains a set of canonical parking spot templates to learn the appearance of a parking lot and estimates the structure of the parking lot from the ...
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A Comparison of Supervised Learning Algorithms for the Income Classification
... After evaluation each classifier individually, the final step is aimed to compare all of the classification models to determine which model is the most effective in census ...
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Efficient prediction of phishing websites using supervised learning algorithms
... performance evaluation based on kappa statistics, mean absolute error, root mean squared error, relative absolute error and root relative squared error is shown in ...
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TRTML - A Tripleset Recommendation Tool based on Supervised Learning Algorithms
... combines supervised learning algorithms and link prediction measures to provide ...The evaluation of the tool adopted as ground truth a set of links obtained from metadata stored in the ...
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Human model evaluation in interactive supervised learning
... Generalization Accuracy and the Choice and Design of Algorithms for IML Supervised learning algorithms are often explicitly designed with the goal of maximizing generalization accuracy. Gen- ...
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Explanation-Oriented Association Mining Using a Combination of Unsupervised and Supervised Learning Algorithms
... and evaluation phase to the data mining ...mining algorithms, we focus on explaining mined ...unsupervised learning that searches for interesting ...and evaluation of mined patterns can be ...
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Supervised Learning Classification Algorithms Comparison
... Under supervised machine learning, classification tasks are one of the most important tasks as a part of data ...end evaluation parameters like confusion matrix, precision, recall, f1-score and ...
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Comparison of Supervised and Unsupervised Learning Algorithms for Brain Tumor Detection
... segmentation algorithms on the basis of the simulation ...various algorithms on the BraTS dataset of 290 ...competing algorithms and techniques available for detection and diagnosis of the tumor, our ...
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Comparing different supervised machine learning algorithms for disease prediction
... TN þ FP False positive rate ¼ FP FP þ TN An ROC is one of the fundamental tools for diagnostic test evaluation and is created by plotting the true posi- tive rate against the false positive rate at various thresh- ...
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Applying Supervised Machine Learning Algorithms for Analytics of Sensor Data
... Machine learning approaches are best suited where ample amount of data are available, but very less is known about the ...Machine learning approaches possesses a capability to vary the outcome according to ...
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Performance Evaluation of Supervised Classification Algorithms Using Data Mining
... mining algorithms which carry out task of the allocating the objects into their predefined classes are known as classifiers ...as supervised learning because classes are determined before analyzing ...
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Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection
... The learning model’s evaluation is based on their accuracy, recall, precision, TPR, FPR, specificity and ...ML learning methods, increase the size of training and testing ...
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A Comparative Study on Mushroom Classification using Supervised Machine Learning Algorithms
... effectiveness evaluation revealed that the model using the Information Gain technique alongside the Random Forest technique provided the most accurate classification outcomes at ...
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Evaluation of Supervised Machine Learning for Classifying Video Traffic
... both algorithms with the same dataset; therefore, to maintain consistency in using Weka implementation of machine learning algorithms, SMO was used for all experiments requiring ...
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Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning
... Max-margin graph cuts algorithm learns max-margin graph cuts that are conditioned on the labels induced by the harmonic function solution. The approach is evaluated on a synthetic problem and three UCI ML repository ...
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Empirical Evaluation of Semi-Supervised Naïve Bayes for Active Learning
... two algorithms were designed for learning semantic words such as apple is a ...Basilisk algorithms start with the non-annotated texts and seed words that are semantic in ...
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A comparison of supervised learning algorithms
... Different values for the amounts of gradient descent interactions and nodes in the hidden layer were chosen arbitrarily. The values for the learning rate were chosen according to a simple suggestion by Mitchell ...
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Weakly Supervised Learning Algorithms and an Application to Electromyography
... weakly supervised classifica- tion is introduced, where a limited number of available labelled instances (those belonging to normal bags of the muscle dataset) and a larger set of unlabelled instances (those ...
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Training Algorithms for Supervised Machine Learning: Comparative Study
... ABSTRACT Supervised machine learning is an important task for learning artificial neural networks; therefore a demand for selected supervised learning algorithms such as back ...
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