Code Monkey home page Code Monkey logo

102flower-training's Introduction

102Flower-Training

机器学习练习,使用SVM、随机森林、Adaboost算法训练 102 flower 训练集 得到特征提取

相关资料

工作记录

  • 2017.04.24 谈合作细节,了解项目相关领域知识(SVM CNN RF Adaboost等) 商讨价格,第一次优化 SVM 程序
  • 2017.04.25 尝试完成 RF 第一版本程序
  • 2017.04.26 第二次优化 SVM 程序
  • 2017.04.27 完善 RF 程序 得到 最佳参数: T 100 M 128
  • 2017.04.28 交接工作 讲解程序**
  • 2017.05.03 先打1500块钱 作为 第一部分交付额
  • 2017.05.08~2017.05.12 关于 RF 原理部分的讨论

Matlab 分类器

  • MATLAB中的分类器

目前了解到的MATLAB中分类器有:K近邻分类器,随机森林分类器,朴素贝叶斯,集成学习方法,鉴别分析分类器,支持向量机。现将其主要函数使用方法总结如下,更多细节需参考MATLAB 帮助文件。


训练样本:train_data             % 矩阵,每行一个样本,每列一个特征
训练样本标签:train_label       % 列向量
测试样本:test_data
测试样本标签:test_label
  • K近邻分类器 (KNN)

    mdl = ClassificationKNN.fit(train_data,train_label,'NumNeighbors',1); predict_label = predict(mdl, test_data); accuracy = length(find(predict_label == test_label))/length(test_label)*100

  • 随机森林分类器(Random Forest)

    B = TreeBagger(nTree,train_data,train_label); predict_label = predict(B,test_data);

  • 朴素贝叶斯 (Na?ve Bayes)

    nb = NaiveBayes.fit(train_data, train_label); predict_label = predict(nb, test_data); accuracy = length(find(predict_label == test_label))/length(test_label)*100;

  • 集成学习方法(Ensembles for Boosting, Bagging, or Random Subspace)

    ens = fitensemble(train_data,train_label,'AdaBoostM1' ,100,'tree','type','classification'); predict_label = predict(ens, test_data);

  • 鉴别分析分类器(discriminant analysis classifier)

    obj = ClassificationDiscriminant.fit(train_data, train_label); predict_label = predict(obj, test_data);

  • 支持向量机(Support Vector Machine, SVM)

    SVMStruct = svmtrain(train_data, train_label); predict_label = svmclassify(SVMStruct, test_data)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.