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Coles DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: NAIVE BAYES, NEAREST NEIGHBORS and NEURAL NETWORKS: Examples with MATLAB at Bayshore Shopping Centre in Ottawa, ON
DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: NAIVE BAYES, NEAREST NEIGHBORS and NEURAL NETWORKS: Examples with MATLAB in Ottawa, ON
DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: NAIVE BAYES, NEAREST NEIGHBORS and NEURAL NETWORKS: Examples with MATLAB in Ottawa, ON
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Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques), which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Classification models predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Typical applications include medical research, fraud detection, and credit scoring. This book develops classification predictive techniques: Naive Bayes, Nearest Neighbors, Pattern Recognition and Neural Networks. Exercises are solved with MATLAB software.
Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques), which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Classification models predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Typical applications include medical research, fraud detection, and credit scoring. This book develops classification predictive techniques: Naive Bayes, Nearest Neighbors, Pattern Recognition and Neural Networks. Exercises are solved with MATLAB software.
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