网站建设公司的成本有哪些方面,长沙个人做网站,商标查询软件,深圳网站建设有免费的吗目录 #x1f4a5;1 概述
#x1f4da;2 运行结果
#x1f389;3 参考文献
#x1f468;#x1f4bb;4 Matlab代码 #x1f4a5;1 概述
机器学习是让计算机在没有明确编程的情况下采取行动的科学。在过去的十年中#xff0c;机器学习为我们提供了自动驾驶汽车1 概述
2 运行结果
3 参考文献
4 Matlab代码 1 概述
机器学习是让计算机在没有明确编程的情况下采取行动的科学。在过去的十年中机器学习为我们提供了自动驾驶汽车实用的语音识别有效的网络搜索以及对人类基因组的理解大大提高。机器学习在今天是如此普遍以至于你可能每天使用它几十次而不自知。许多研究人员还认为这是朝着人类水平的人工智能取得进展的最佳方式。在本代码中您将了解最有效的机器学习技术并获得实施它们并让它们为自己工作的练习。更重要的是您不仅将学习学习的理论基础还将获得快速有效地将这些技术应用于新问题所需的实践知识。最后您将了解硅谷在创新方面的一些最佳实践因为它与机器学习和人工智能有关。本代码广泛介绍了机器学习、数据挖掘和统计模式识别。主题包括i监督学习参数/非参数算法支持向量机内核神经网络。ii无监督学习聚类、降维、推荐系统、深度学习。iii机器学习的最佳实践偏差/方差理论;机器学习和人工智能的创新过程。本课程还将借鉴众多案例研究和应用以便您还将学习如何应用学习算法来构建智能机器人感知、控制、文本理解网络搜索、反垃圾邮件、计算机视觉、医学信息学、音频、数据库挖掘和其他领域。
2 运行结果 主函数部分代码
%% Machine Learning Online Class
% Exercise 6 | Spam Classification with SVMs
%
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% exercise. You will need to complete the following functions:
%
% gaussianKernel.m
% dataset3Params.m
% processEmail.m
% emailFeatures.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
% %% Initialization
clear ; close all; clc %% Part 1: Email Preprocessing
% To use an SVM to classify emails into Spam v.s. Non-Spam, you first need
% to convert each email into a vector of features. In this part, you will
% implement the preprocessing steps for each email. You should
% complete the code in processEmail.m to produce a word indices vector
% for a given email. fprintf(\nPreprocessing sample email (emailSample1.txt)\n); % Extract Features
file_contents readFile(emailSample1.txt);
word_indices processEmail(file_contents); % Print Stats
fprintf(Word Indices: \n);
fprintf( %d, word_indices);
fprintf(\n\n); fprintf(Program paused. Press enter to continue.\n);
pause; %% Part 2: Feature Extraction
% Now, you will convert each email into a vector of features in R^n.
% You should complete the code in emailFeatures.m to produce a feature
% vector for a given email. fprintf(\nExtracting features from sample email (emailSample1.txt)\n); % Extract Features
file_contents readFile(emailSample1.txt);
word_indices processEmail(file_contents);
features emailFeatures(word_indices); % Print Stats
fprintf(Length of feature vector: %d\n, length(features));
fprintf(Number of non-zero entries: %d\n, sum(features 0)); fprintf(Program paused. Press enter to continue.\n);
pause; %% Part 3: Train Linear SVM for Spam Classification
% In this section, you will train a linear classifier to determine if an
% email is Spam or Not-Spam. % Load the Spam Email dataset
% You will have X, y in your environment
load(spamTrain.mat); fprintf(\nTraining Linear SVM (Spam Classification)\n)
fprintf((this may take 1 to 2 minutes) ...\n) C 0.1;
model svmTrain(X, y, C, linearKernel); p svmPredict(model, X); fprintf(Training Accuracy: %f\n, mean(double(p y)) * 100); %% Part 4: Test Spam Classification
% After training the classifier, we can evaluate it on a test set. We have
% included a test set in spamTest.mat % Load the test dataset
% You will have Xtest, ytest in your environment
load(spamTest.mat); fprintf(\nEvaluating the trained Linear SVM on a test set ...\n) p svmPredict(model, Xtest); fprintf(Test Accuracy: %f\n, mean(double(p ytest)) * 100);
pause; %% Part 5: Top Predictors of Spam
% Since the model we are training is a linear SVM, we can inspect the
% weights learned by the model to understand better how it is determining
% whether an email is spam or not. The following code finds the words with
% the highest weights in the classifier. Informally, the classifier
% thinks that these words are the most likely indicators of spam.
% % Sort the weights and obtin the vocabulary list
[weight, idx] sort(model.w, descend);
vocabList getVocabList(); fprintf(\nTop predictors of spam: \n);
for i 1:15 fprintf( %-15s (%f) \n, vocabList{idx(i)}, weight(i));
end fprintf(\n\n);
fprintf(\nProgram paused. Press enter to continue.\n);
pause; %% Part 6: Try Your Own Emails
% Now that youve trained the spam classifier, you can use it on your own
% emails! In the starter code, we have included spamSample1.txt,
% spamSample2.txt, emailSample1.txt and emailSample2.txt as examples.
% The following code reads in one of these emails and then uses your
% learned SVM classifier to determine whether the email is Spam or
% Not Spam % Set the file to be read in (change this to spamSample2.txt,
% emailSample1.txt or emailSample2.txt to see different predictions on
% different emails types). Try your own emails as well!
filename spamSample1.txt; % Read and predict
file_contents readFile(filename);
word_indices processEmail(file_contents);
x emailFeatures(word_indices);
p svmPredict(model, x); fprintf(\nProcessed %s\n\nSpam Classification: %d\n, filename, p);
fprintf((1 indicates spam, 0 indicates not spam)\n\n); 3 参考文献
[1]谢宜鑫. 基于机器学习的建筑空调能耗数据挖掘和模式识别[D].北京交通大学,2019.
4 Matlab代码
