Supervised learning neural networks

Radiotherapy has been widely used to treat various cancers, but its efcacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity.

15p vileonardodavinci 23-12-2023 6 3   Download

This course is an introduction to the subject of Artificial Neural Networks and Genetic Algorithms, two very new subjects forming part of Distributed Artificial Intelligence. As you leaf through these notes you will notice that they are full of mathematical equations. The reason is simple: these subjects are inherently mathematical. However the course and assessments are such that it will be possible for you to pass if you do not touch the equations. However if you wish to gain a good pass you must attempt to master the equations.

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Ebook "Natural language processing with PyTorch: Build intelligent language applications using deep learning" aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground.

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Why this book on computational intelligence? Need arose from a graduate course, where students do not have a deep background of artificial intelligence and mathematics. Therefore the introductory nature, both in terms of the CI paradigms and mathematical depth. While the material is introductory in nature, it does not shy away from details, and does present the mathematical foundations to the interested reader.

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This paper proposes a Convolutional Neural Network (CNN)- based detection model for website defacements. The model is an extension of previous models based on traditional supervised machine learning techniques and its aims are to improve the detection rate and reduce the false alarm rate.

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Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode. EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children’s Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs.

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Lecture Introduction to Machine learning and Data mining: Lesson 8. This lesson provides students with content about: supervised learning; artificial neural network; structure of a neuron; activation function - hard-limited; rectified linear unit;... Please refer to the detailed content of the lecture!

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The study aims to apply the supervised machine learning method to the classification of product review content in online customer comment mining. The entire study was conducted automatic data collection with 2,241 customer reviews on products on Lazada.vn, then trained with Supervised Machine Learning models to find the most suitable model with the training dataset and apply this model to predict the reviews content for the dataset.

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Colonoscopy image classification is an image classification task that predicts whether colonoscopy images contain polyps or not. It is an important task input for an automatic polyp detection system. Recently, deep neural networks have been widely used for colonoscopy image classification due to the automatic feature extraction with high accuracy.

11p vikissinger 03-03-2022 11 1   Download

In the proposed solution, Random Forests are suggested as an e®ective ensemble model suitable for our heterogeneous data when many single prediction models which are random trees can be built for many various subspaces with di®erent random features in a supervised learning process.

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Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as transcriptomes and metabolomes, have long existed as a valuable resource for facilitating diagnosis of patient statuses/stages.

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Automatic text classification is one of the most interesting task in data mining. This task has to deal with a huge amount of data. Many studies have been investigated for English, however, the investigation of Vietnamese is still an early stage. This paper investigates several text classification methods: Super Vector Machine, Naive Bayes Classification, K-Nearest Neighbors, Multi-layer perceptron, Decision Tree, Random Forest using TF-IDF. The experiments in Vietnamese datasets show that Super Vector Machine and Multi-layer perceptron perform better than the other methods.

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The techniques of supervised ones are applied to the data domain in order to have a comparison between the evaluated system of POSSUM and the advantage of Neural network. The comparisons are based on the rate of mortality and morbidity of patients. The outcome set of unsupervised learning techniques is compared to the results of supervised ones.

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Nội dung môn học gồm lý thuyết và thực hành: 1. Lý thuyết đưa ra các chủ đề về neural network và ứng dụng vào quá trình học có/không có giám sát (supervised/unsupervised learning) 2. Thực hành với Matlab và ứng dụng của thuật toán học trong neural network. 3. Các ứng dụng của Neural Networks trong lĩnh vực điện tử-viễn thông.

40p haiph37 15-09-2010 167 56   Download

LEARNING SHAPE AND MOTION FROM IMAGE SEQUENCES Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Sue Becker and Ron Racine Department of Psychology, McMaster University, Hamilton, Ontario, Canada (beckers@mcmaster.ca) 3.1 INTRODUCTION In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images.

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In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images. Many complexities arise in visual processing that are not present in onedimensional prediction problems: the scene may be cluttered with backKalman Filtering and Neural Network

13p duongph05 07-06-2010 79 14   Download