query: Why is SVM sensitive to scaling of features?
positive_passages.docid: 142623
positive_passages.text: scaling for SVM destroys my results
negative_passages.docid: 106596
negative_passages.text: Why L1 norm for sparse models
negative_passages.docid: 28133
negative_passages.text: Theoretical: Minimum Number of Support Vectors
negative_passages.docid: 135800
negative_passages.text: Validation of Models with Different Scale Data
negative_passages.docid: 57565
negative_passages.text: Scaling data that are on different orders of magnitude for plotting
negative_passages.docid: 107278
negative_passages.text: Comparing SVM and logistic regression
negative_passages.docid: 72501
negative_passages.text: What does the "machine" in "support vector machine" and "restricted Boltzmann machine" mean?
negative_passages.docid: 14183
negative_passages.text: Why Lasso or ElasticNet perform better than Ridge when the features are correlated
negative_passages.docid: 158632
negative_passages.text: About SVM cost and gamma parameters tuning
negative_passages.docid: 147748
negative_passages.text: Issues with feature selection in matlab
negative_passages.docid: 19801
negative_passages.text: Input vector representation vs output vector representation in word2vec
negative_passages.docid: 56397
negative_passages.text: Why do smaller weights result in simpler models in regularization?
negative_passages.docid: 136012
negative_passages.text: why boosting method is sensitive to outliers
negative_passages.docid: 57060
negative_passages.text: Is normalizing the features always good for classification?
negative_passages.docid: 108285
negative_passages.text: Origin and formation to the ERROR ‘Dimension too large’
negative_passages.docid: 107608
negative_passages.text: Differentiation in support vector machine
negative_passages.docid: 22097
negative_passages.text: How do I disable negative scaling?