I've always found Andrew Moore's Tutorials to be very useful. They're grounded in solid statistical theory and will be very useful in understanding papers if you choose to read them in the future. Here's a short description:
These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. They include regression algorithms such as multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and neural nets. And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning
The answer referring to Andrew Moore's tutorials is a good one. I'd like to augment it, however, by suggesting some reading on the need which drives the creation of many classification systems in the first place: identification of causal relationships. This is relevant to many modeling problems involving statistical inference.
This course (CS229) -- taught by Professor Andrew Ng -- provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
Classifiers
As for which classifier you should use, I'd recommend first starting with Support Vector Machines (SVM) for general applied classification tasks. They'll give you state-of-the-art performance, and you don't really need to understand all of the theory behind them to just use the implementation provided by a package like WEKA.
If you have a larger data-set, you might want to try using Random Forests. There's also an implementation of this algorithm in WEKA, and they train much faster on large data. While they're less broadly used than SVMs, their accuracy tends to match or nearly match the accuracy you could get from one.
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我一直发现Andrew Moore 的教程非常有用。它们以扎实的统计理论为基础，如果您将来选择阅读它们，对于理解论文将非常有用。这是一个简短的描述：
I've always found Andrew Moore's Tutorials to be very useful. They're grounded in solid statistical theory and will be very useful in understanding papers if you choose to read them in the future. Here's a short description:
参考安德鲁摩尔教程的答案是一个很好的答案。然而，我想通过建议阅读一些关于推动许多分类系统创建的需求来补充它：因果关系的识别。这与涉及统计推断的许多建模问题相关。
据我所知，目前学习因果关系和分类器系统（尤其是贝叶斯分类器）的最佳资源是 Judea Pearl 的书《因果关系：模型、推理和推论》。
The answer referring to Andrew Moore's tutorials is a good one. I'd like to augment it, however, by suggesting some reading on the need which drives the creation of many classification systems in the first place: identification of causal relationships. This is relevant to many modeling problems involving statistical inference.
The best current resource I know of for learning about causality and classifier systems (especially Bayesian classifiers) is Judea Pearl's book "Causality: models, reasoning, and inference".
机器学习概述
要全面了解该领域，请观看 吴恩达的机器学习课程。
分类器
至于您应该使用哪个分类器，我建议首先从支持向量机 (SVM) 用于一般应用分类任务。它们将为您提供最先进的性能，并且您实际上不需要了解它们背后的所有理论，只需使用 WEKA 等包提供的实现即可。
如果您有更大的数据集，您可能需要尝试使用随机森林 。 WEKA 中还有此算法的实现，并且它们在大数据上训练更快。虽然它们的使用不如 SVM 广泛，但它们的准确性往往与您可以从 SVM 获得的准确性相匹配或接近匹配。
Overview of Machine Learning
To get a good overview of the field, watch the video lectures of Andrew Ng's Machine Learning course.
Classifiers
As for which classifier you should use, I'd recommend first starting with Support Vector Machines (SVM) for general applied classification tasks. They'll give you state-of-the-art performance, and you don't really need to understand all of the theory behind them to just use the implementation provided by a package like WEKA.
If you have a larger data-set, you might want to try using Random Forests. There's also an implementation of this algorithm in WEKA, and they train much faster on large data. While they're less broadly used than SVMs, their accuracy tends to match or nearly match the accuracy you could get from one.