【NLP】SVM

1. SVM的原理

2. SVM应用场景

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

3. SVM优缺点

• The advantages of support vector machines are:
  • Effective in high dimensional spaces.
  • Still effective in cases where number of dimensions is greater than the number of samples.
  • Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
  • Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
• The disadvantages of support vector machines include:
  • If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial.
  • SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below).

4. SVM sklearn 参数学习

sklearn官网

5. 利用SVM模型结合 Tf-idf 算法进行文本分类

代码实现