Subset of Features used for Machine learning
The influence of the inactives subset generation on the performance of machine learning methods
... by machine learning ...structures, machine learning algorithms are unable to select correctly potentially active compounds, when they are trained on in- active molecules covering a chemical ...
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Using Different Machine Learning Techniques for Predicting the Price of Used Cars
... in Machine Learning: single model trees are combined with Random Forest ...random subset of all attributes is considered at every node, and the best split for that subset is ...
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Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection
... 59 features representation for the AE ...be used when the data necessitates a highly non-linear feature ...a subset of training samples and a subset of variables for splitting at each tree ...
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Student Outcomes Prediction by used of Machine Learning
... Education, Machine Learning ...conclusion. Machine learning can be effective in institutions when assigning outcomes since it can be used to analyse data and construct models that can ...
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Machine Learning Algorithms Used for Adaptive Modelling
... of learning materials to the needs and behavior of each ...selected learning objects among students with ...of learning centric environment is based on the specific characteristics of students and ...
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Comparison of SIFT & SURF Corner Detector as Features and other Machine Learning Techniques for Identification of Commonly used Leaves
... extract features, clustering algorithm to cluster the features and decision trees as a ...are used to build descriptor that is the vector of feature for each corner ...are used to cluster the ...
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Machine Learning Techniques used for Analysis of Air Quality
... supervised learning technique which uses a predictive model to map observations about an item (represented in the branches) to conclusions about the item’s target value (represented in the ...from features ...
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Machine Learning Techniques for learning features of any kind of data: A Case Study
... Abstract— Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that ...
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Generic object recognition by combining distinct features in machine learning
... 1 used sparse network of Winnows as the learning algorithm for the recognition of cars from side ...These features were obtained by moving a window in the whole image and sensitive for the image with ...
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Generic object recognition by combining distinct features in machine learning
... is used to retrieve the ...was used to learn the semantic feature space between interest point and key point features from the same image and produce a new kernel function for ...
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On explaining machine learning models by evolving crucial and compact features
... of Machine Learning (ML) ...evolved features is not explicitly bound or minimized though this is arguably key for ...constructing features that are (1) Of small-enough number, to enable ...
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Learning Invariant Features Using Subspace Restricted Boltzmann Machine
... Jakub M. Tomczak 1 · Adam Gonczarek 1 Published online: 7 April 2016 © The Author(s) 2016. This article is published with open access at Springerlink.com Abstract The subspace restricted Boltzmann machine ...
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Question Classification Using Extreme Learning Machine on Semantic Features
... statistical machine learning approaches for question classification, efforts based on lexical feature space require high computation power and complex data ...semantic features instead could ...
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Identifying Features of Video Games That Are Most Suited to Machine Learning
... the features found were demonstrated in video games, but could also be applied to other reinforcement based neural network ...game‟s features, as well as changing what information is presented to the ...
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Learning features for tissue classification with the classification restricted Boltzmann machine
... There are two ways to use RBMs in a classification problem. The standard RBM is an unsupervised model without label information. It learns a represen- tation that can be used as the input for an external ...
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Optimal Features Subset Selection and Classification for Iris Recognition
... optimal features subset and the classification have become an important issue in the field of iris ...vector machine for the classification of iris ...is used to train the support vector ...
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Supervised Machine Learning Algorithm Used to Detect Fault in an Induction Motor
... . Key Words: AI, ANN, SVM 1.INTRODUCTION This report is template. The induction motor is one of the maximum crucial vehicles used in commercial applications[13]. Its low price and high overall performance in ...
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Performance Evaluation of Several Machine Learning Techniques Used in the Diagnosis of Mammograms
... diseases. Machine learning(ML) approach has been widely used for the diagnosis of benign and malignant masses in the ...various machine learning techniques the diagnosis of benign and ...
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First Notification of Loss (FNOL) Machine Learning Process Used for Telematics
... the Machine Learning ...for machine learning process and finally send the mail to the respective mail distribution list informing about the accident and the accident ...
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Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis
... “active learning” approach, it is equally im- portant to optimize the creation of the initial ML model given less training data so that the model is able to capture most if not all posi- tive examples, and filter ...
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