OpenCV机器学习()(英文版)
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浙江嘉兴
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作者(美)迈克尔·贝耶勒
出版社东南大学
ISBN9787564183240
出版时间2019-05
装帧其他
开本其他
定价96元
货号30616852
上书时间2024-10-22
商品详情
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目录
Preface
Chapter 1：A Taste of Machine Learning
Getting started with machine learning
Problems that machine learning can solve
Getting started with Python
Getting started with OpenCV
Installation
Getting the latest code for this book
Getting to grips with Python's Anaconda distribution
Installing OpenCV in a conda environment
Verifying the installation
Getting a glimpse of OpenCV's ML module
Summary
Chapter 2: Working with Data in OpenCV and Python
Understanding the machine learning workflow
Dealing with data using OpenCV and Python
Starting a new IPython or Jupyter session
Dealing with data using Python's NumPy package
Importing NumPy
Understanding NumPy arrays
Accessing single array elements by indexing
Creating multidimensional arrays
Loading external datasets in Python
Visualizing the data using Matplotlib
Importing Matplotlib
Producing a simple plot
Visualizing data from an external dataset
Dealing with data using OpenCV's TrainData container in C++
Summary
Chapter 3: First Steps in Supervised Learning
Understanding supervised learning
Having a look at supervised learning in OpenCV
Measuring model performance with scoring functions
Scoring classifiers using accuracy, precision, and recall
Scoring regressors using mean squared error, explained variance, and R squared
Using classification models to predict class labels
Understanding the k-NN algorithm
Implementing k-NN in OpenCV
Generating the training data
Training the classifier
Predicting the label of a new data point
Using regression models to predict continuous outcomes
Understanding linear regression
Using linear regression to predict Boston housing prices
Loading the dataset
Training the model
Testing the model
Applying Lasso and ridge regression
Classifying iris species using logistic regression
Understanding logistic regression
内容摘要
本书首先介绍了统计学习的基本概念，例如分类和回归。介绍完所有的基础知识之后，就开始探究如
决策树、支持向量机、贝叶斯网络等算法，学习如何将它们与其他OpenCV功能综合运用。你的机器学习技
能会随着书中内容的进度一同提高，直到准备好学习当前最热门的主题：深度学习。在本书的结尾，你可以根据现有的源代码构建或是从头开发自己的算法来解决自己碰到的机器学习问题！
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