支持矢量机还是人工神经网络进行文本处理? [英] Support Vector Machine or Artificial Neural Network for text processing?

问题描述

对于某些文本处理项目，我们需要在支持向量机和快速人工神经网络之间做出决定.

We need to decide between Support Vector Machines and Fast Artificial Neural Network for some text processing project.

它包括上下文拼写校正，然后将文本标记为某些短语及其同义词.

It includes Contextual Spelling Correction and then tagging the text to certain phrases and their synonyms.

哪种方法是正确的?还是有这两种方法的替代方案?比FANN和SVM更合适的方法?

Which will be the right approach? Or is there an alternate to both of these... Something more appropriate than FANN as well as SVM?

推荐答案

我认为您会从这两种算法中获得有竞争力的结果，因此您应该汇总结果...考虑集成学习.

I think you'll get a competitive results from both of the algorithms, so you should aggregate the results... think about ensemble learning.

更新:
我不知道这是否足够具体:使用贝叶斯最佳分类器组合每种算法的预测.您必须训练两种算法，然后必须训练贝叶斯最佳分类器以使用算法并根据算法的输入做出最佳预测.

Update:
I don't know if this is specific enough: use Bayes Optimal Classifier to combine the prediction from each algorithm. You have to train both of your algorithms, then you have to train the Bayes Optimal Classifier to use your algorithms and make optimal predictions based on the input of the algorithms.

在3中分开训练数据

• 第一个数据集将用于训练(人工)神经网络和支持向量机.
• 第二个数据集将通过从ANN和SVM中获取原始预测来训练贝叶斯最优分类器.
• 第三个数据集将是您的资格认证数据集，您将在其中测试训练有素的贝叶斯最佳分类器.

更新2.0:
创建算法集合的另一种方法是使用 10倍(或更多)通常是k折)交叉验证:

Update 2.0:
Another way to create an ensemble of the algorithms is to use 10-fold (or more generally, k-fold) cross-validation:

• 将数据分成10组，大小为n/10.
• 训练9个数据集并进行1个测试.
• 重复10次并取得平均准确度.

请记住，通常可以将许多分类器和验证方法结合起来以产生更好的结果.只是找到最适合您的域的问题.

Remember that you can generally combine many the classifiers and validation methods in order to produce better results. It's just a matter of finding what works best for your domain.

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