SVM 可以增量学习吗？
我正在使用多维 SVM 分类器（SVM.NET，libSVM 的包装器）对一组特征进行分类。
给定 SVM 模型，是否可以合并新的训练数据而无需重新计算所有先前的数据？我想另一种说法是：SVM 是可变的吗？
I am using a multi-dimensional SVM classifier (SVM.NET, a wrapper for libSVM) to classify a set of features.
Given an SVM model, is it possible to incorporate new training data without having to recalculate on all previous data? I guess another way of putting it would be: is an SVM mutable?
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实际上，这通常称为增量学习。这个问题之前已经出现过，在这里得到了很好的回答：支持向量机 (SVM) 的一些实现细节。
简而言之，这是可能的，但并不容易，您必须更改正在使用的库或自己实现训练算法。
我找到了两种可能的解决方案， SVMHeavy 和 LaSVM，支持增量训练。但我没用过，也不了解它们。
Actually, it's usually called incremental learning. The question has come up before and is pretty well answered here : A few implementation details for a Support-Vector Machine (SVM).
In brief, it's possible but not easy, you would have to change the library you are using or implement the training algorithm yourself.
I found two possible solutions, SVMHeavy and LaSVM, that supports incremental training. But I haven't used either and don't know anything about them.
在线和增量虽然相似但略有不同。在线上，一般可以配置单次（epoch=1）或epoch数。增量意味着你已经有了一个模型；不管它是如何构建的，但是模型可以通过新的示例来改变。此外，通常还需要在线和增量的结合。
以下是一些工具列表，其中包含有关在线和/或增量 SVM 的一些注释：https://stats.stackexchange.com/questions/30834/is-it-possible-to-append-training-data-to-existing-svm-models/ 51989#51989
Online and incremental although similar but differ slightly. In online, its generally a single pass(epoch=1) or number of epochs could be configured. Where as, incremental would mean that you already have a model; no matter how it is built, but then model can be mutable by new examples. Also, a combination of online and incremental is often what is required.
Here is a list of tools with some remarks on the online and/or incremental SVM : https://stats.stackexchange.com/questions/30834/is-it-possible-to-append-training-data-to-existing-svm-models/51989#51989