TAILIEUCHUNG - COMPUTING RESEARCH FOR SUSTAINABILITY

Tham khảo sách 'computing research for sustainability', y tế - sức khoẻ, y học thường thức phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | COMPUTING research FOR sustainability Lynette I. Millett and Deborah L. Estrin Editors Committee on Computing Research for Environmental and Societal Sustainability Computer Science and Telecommunications Board Division on Engineering and Physical Sciences NATIONAL RESEARCH COUNCIL Of THE NATIONAL ACADEMIES THE NATIONAL ACADEMIES PRESS Washington . THE NATIONAL ACADEMIES PRESS 500 Fifth Street NW Washington DC 20001 NOTICE The project that is the subject of this report was approved by the Governing Board of the National Research Council whose members are drawn from the councils of the National Academy of Sciences the National Academy of Engineering and the Institute of Medicine. The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance. Support for this project was provided by the National Science Foundation under award 115-0950451. Any opinions findings conclusions or recommendations expressed in this publication are those of the authors and do not necessarily re ect the views of the organization that provided support for the project. International Standard Book Number-13 978-0-309-25758-9 International Standard Book Number-10 0-309-25758-1 Copies of this report are available from The National Academies Press 500 Fifth Street NW Keck 360 Washington DC 20001 800 624-6242 or 202 334-3313 http Copyright 2012 by the National Academy of Sciences. All rights reserved. Printed in the United States of .

• Y học thường thức
• Machine Learning
• Adaptive Systems
• Challenge Areas
• Industrial Interest
• Virtual Worlds
• Computer Interface
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Max margin learning
• Multi instance learning
• Reinforcement learning
• Delayed rewards
• Machine Learning Introduction
• Machine Learning Work
• Types of Machine Learning
• Supervised Learning
• Forms of Supervised Learning
• Bayesian Learning
• Back propagation
• Boltzmann machines
• Linear prediction
• Linear regression models
• Maximum likelihood
• Gaussian distributions
• Nonlinear ridge regression risk
• Cross validation
• Newton’s algorithm
• Gradient descent algorithm
• Logistic regression
• Logistic regression likelihood
• Formulate layers
• Neural networks
• Artificial neural networks
• Convolutional neural networks
• Design convolutional layers
• Recurrent nets
• Recurrent neural networks
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Linear regression
• Learning a regression function
• Unsupervised learning
• Partition based clustering
• The sewing machine classroom
• Learning the ins
• Outs of your machine
• Mastering the materials
• Demystifying your machine
• Increasing your sphere
• Deep Belief Network
• Restricted Boltzmann Machine
• Support Vector Machine
• Automatic CNC program
• Challenges in Machine Learning
• Data Mining
• Learning from non vectorial data
• Beyond classification and regression
• Distributed data mining
• Machine learning bottleneck
• Introduction to machine learning
• Bayesian decision theory
• Parametric methods
• Multivariate methods
• BMC Bioinformatics
• Extreme learning machine
• Correntropy induced loss
• Machine learning method
• K nearest neighbors
• Neighbor based learnin
• Decision tree
• Random forest
• Support vector machines
• Separating hyperplane
• Artificial neural network
• Rectified linear unit
• Normalization methods
• Deep learning
• Representation learning
• Phenotype prediction
• Molecular diagnostics