blocks|key|2853173|text|如果你想从一些简单的东西开始，考虑一个准线性模型，比如逻辑回归或线性判别分析:它们很容易理解，而且互联网上到处都有针对它们的代码。还要考虑一些更简单的(单节点)神经模型(感知器、增量规则等)：它们非常容易编程。如果你想追求这一点，我建议你买一本书，比如Weiss和Kulikowski写的“学习的计算机系统”。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2853174|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

If you want to begin with something simple, consider a quasi-linear model, such as logistic regression or linear discriminant analysis: they are easy to understand, and there is code for them all over the Internet. Also consider some of the simpler (single node) neural models (perceptron, delta rule, etc.): they are very easy to program. If you want to pursue this, I suggest getting a book, such as "Computer Systems That Learn", by Weiss and Kulikowski.

blocks|key|2853110|text|斯坦福大学有一个很好的关于机器学习的公开课，有视频讲座等。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2853111|看看here吧。|offset|length|2853112|entityMap|0|LINK|mutability|MUTABLE|url|http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1^0|0|2|4|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

There is a good Stanford Open Course about machine learning with video lectures etc. 
Take a look <a href="http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1" rel="noreferrer">here</a>.

blocks|key|4453194|text|也许你可以在维基百科上搜索各种classification+algorithms，比如k-nearest-neighbour、SVM或neural+network|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4453195|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Statistical_classification|1|http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm|2|http://en.wikipedia.org/wiki/Support_vector_machine|3|http://en.wikipedia.org/wiki/Neural_network^0|F|P|0|17|J|1|1R|3|2|1V|E|3|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]|$A|V|B|W|1|X]|$A|Y|B|Z|1|10]|$A|11|B|12|1|13]]|C|$]]|$1|D|3|-4|5|6|7|14|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]|P|$5|G|H|I|C|$J|Q]]]]

Maybe you can start by searching wikipedia for various <a href="http://en.wikipedia.org/wiki/Statistical_classification" rel="nofollow">classification algorithms</a> like <a href="http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm" rel="nofollow">k-nearest-neighbour</a>, <a href="http://en.wikipedia.org/wiki/Support_vector_machine" rel="nofollow">SVM</a> or <a href="http://en.wikipedia.org/wiki/Neural_network" rel="nofollow">neural network</a>

blocks|key|785396|text|我还会从K-Neighbours开始-它们最简单-可以尝试不同的数据预处理，距离测量等。它们也会导致非常好的预测(尽管非常慢，非常慢)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|785397|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I'd also start with K-Nearest-Neighbours - they are most simple - and one can experiment with different data-preprocessings, distance-measures, etc. They also lead to very good (although very very slow) predictions.

blocks|key|785417|text|如果要预测的变量是连续变量，则回归模型是关键。有许多回归技术，包括最小二乘，多项式模型，人工神经网络和支持向量机。当然，每种技术都可能有其假设或参数。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|785418|MATLAB是一种文档齐全的计算环境。我建议访问有关非线性回归的MATLAB文档的以下页面：http://www.mathworks.com/help/stats/nonlinear-regression-1.html#btcgzas-1|offset|length|785419|您可以从使用全局搜索方法开始，例如遗传算法遗传算法，以调整给定多项式回归模型的参数。|785420|对于预测离散变量，列出的回归模型也可以应用于给定阈值。决策树可以是一个很好的选择。|785421|entityMap|0|LINK|mutability|MUTABLE|url|http://www.mathworks.com/help/stats/nonlinear-regression-1.html#btcgzas-1^0|0|1A|21|0|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@$D|T|E|U|1|V]]|A|$]]|$1|F|3|G|5|6|7|W|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|X|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

If the variable to be predicted is a continuous one, then regression models are the key.
Many regression techniques are there including least squares, polynomial models, ANN and SVM.
Of course, every technique may have its assumption or parameters.

MATLAB is one of the well-documented computing environments.
I would advise visiting the following page of the MATLAB documentation on nonlinear regression:
<a href="http://www.mathworks.com/help/stats/nonlinear-regression-1.html#btcgzas-1" rel="nofollow">http://www.mathworks.com/help/stats/nonlinear-regression-1.html#btcgzas-1</a>

You may start by using a global search method such as genetic algorithms GAs to tune the parameters of a given polynomial regression model.

For predicting discrete variables, the listed regression models can be applied also given a threshold. Decision trees can be a good alternative.

blocks|key|4453503|text|Weka将满足您的需求。它具有回归功能，并用Java实现。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4453504|entityMap|0|LINK|mutability|MUTABLE|url|http://www.cs.waikato.ac.nz/ml/weka/^0|0|4|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

<a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow">Weka</a> would fit your need. It has regression and is implemented in Java.

blocks|key|785382|text|听起来像是多元线性回归可以完成这项工作。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|785383|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

sounds like multi variate linear regression would do the job .

blocks|key|2853304|text|在深入研究代码之前，由于您是一个初学者，我建议您阅读基础知识，并对此有一个很好的理解。你不需要阅读PhD论文，但至少支持向量机、逻辑回归和神经网络中的基本术语会有所帮助。通过斯坦福大学，Coursera课程和其他答案中建议的书籍，在互联网上有大量的材料。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2853305|尽管互联网上有现成的代码可供您使用，但我之所以说您需要阅读基础知识，是因为在典型的分类器中，如SVM、神经网络甚至Logistic回归，您需要调整各种参数，而如果不了解基础知识，使用这些软件包将是困难和混乱的。当我还是个初学者的时候，我也有过同样的经历。|2853306|通过掌握如何在SVM中处理倾斜的数据集，如何调整Logistic回归的参数，甚至如何降低数据集的维数，它将使您的实施更快、更有效-这样您就可以获得更高的准确性。否则，直接进入代码可能会让你再次带着一些基本的问题回到这里。我希望这对你有帮助！|2853307|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Before diving into the code, since you are a beginner, I would suggest you read on the fundamentals and gain a strong hold on that. You needn't read a PhD thesis but at least the basic terminologies in SVMs, Logistic Regression and Neural Networks would be helpful. There is plenty of material on internet via Stanford, Coursera courses and books suggested in other answers.

Even though there is ready made code available for you to use on the internet, the reason why I am saying you need to read the fundamentals is because in a typical classifier such as SVM, Neural Network or even Logistic Regression, there are various parameters that you would be required to tune, and without an understanding of the fundamentals, it would be difficult and confusing to use these packages. I experienced the same when I was a beginner.

With a strong hold on how to handle a skewed data set in SVM, how to tune the parameters of a Logistic regression, and even how to reduce the dimensions of your dataset, it would make your implementation faster and more efficient - that way you can get better accuracy. Otherwise, diving straight into code may make you come back here with some basic questions again. I hope this was helpful!

blocks|key|1745406|text|如果这是一个回归问题，我建议你从Matlab中的逻辑回归或线性回归开始。有一些库，你可以为它获取所有的代码。通过这种方式，首先通过比较样本内错误(来自您考虑用于生产的数据)和样本外错误(针对未考虑进行预测的数据来测试您的预测)来测试和查找您需要的特征的数量和顺序以及训练数据量。如果训练数据较少，则使用较少的特征或正则化。如果特征的数量和顺序非常大，并且很难确定，请转向神经网络或SVM+(请参见，如果有用于Java的SVM库)，当您在Matlab中有一个完美的系统时，然后在Java中部署它。据我所知，ML系统在适合实际使用之前需要进行一些手动微调，而像Matlab/Ocatve这样的环境是进行这种微调的最佳平台。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1745407|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

If it's a regression problem, I would suggest you to start with things like logistic or linear regression in Matlab. There are libraries and you can get code all around for it. By this way, first test and find by comparing in-sample error (from data you consider for production) and out-of-sample error (to test your predictions against data that was not considered for making those predictions) the number and order of features and amount of training data you need. If training data is less, use less features or regularization. If number and order of features is very large and difficult to determine, move to neutral networks or SVM(See, if there is an SVM library fo java) and when you have a perfect system in Matlab then deploy it in Java.
As far as I have seen, ML systems require a good bit of manual fine tuning before they become fit for practical use and environments like Matlab/Ocatve are best platforms for this fine tuning.

I am developing a tool where I need to predict a the value of a variable based on the combination of a number of variables.

The implementation needs to be in Java programming language.

Thanks.

Basic machine learning

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在开发一个工具，我需要根据一系列变量的组合来预测变量的值。实现需要使用Java编程语言。谢谢。

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