SVM为每个输入输出相同的预测值...为什么是这样？

Question

我使用Encog，我使用SVM预测数据。我的训练集值没有进行归一化处理，但它们最初在[-1,1]范围内。我不明白为什么会出现这个问题。

我的训练数据：

EURUSD_OPEN_CH,EURUSD_HIGH_CH,EURUSD_LOW_CH,EURUSD_CLOSE_CH,EURUSD_MACD,EURUSD_MACDS,EURUSD_STTDEV 
0.0134883819,0.0132838637,0.0135361889,0.0140344719,0.0023983892,0.0010403195,0.0054870487 
0.0001454143,0.0000969039,-0.0002216665,-0.0005261919,0.0035244907,0.0013168603,0.0070012526 
-0.0005261846,0.0006574986,0.0001593581,0.0009628839,0.0044774819,0.0017225556,0.0081131621 
0.0009282350,-0.0001867452,-0.0004156506,-0.0005882475,0.0051052958,0.0021969854,0.0088044648 
-0.0005605769,-0.0006641071,0.0001455382,0.0000069246,0.0055397905,0.0027231400,0.0092672117 
(...) 

我应该正常化这些价值？我认为这不是一个问题，但是谁知道......我训练SVM并且一切正常，但是当我评估SVM时，每个输入的输出都是相同的。如果需要的话，我可以附上代码。

Answer 1

我是这样一个noob ...规范化解决了这个问题。这些值太小而无法预测，所以我将整个CSV归一化到了[0.1,0.9]范围，并且它有所帮助。