query: Why Do Support Vector Machines Require 3 Support Vectors?
positive_passages.docid: 28133
positive_passages.text: Theoretical: Minimum Number of Support Vectors
negative_passages.docid: 55420
negative_passages.text: How does the chain rule work for functions from vectors to vectors?
negative_passages.docid: 34737
negative_passages.text: Optimal number of folds in $K$-fold cross-validation: is leave-one-out CV always the best choice?
negative_passages.docid: 865
negative_passages.text: Generalized RBF Kernels
negative_passages.docid: 156500
negative_passages.text: SVM for unbalanced data
negative_passages.docid: 89330
negative_passages.text: Machine Learning - Where is the difference between one-class, binary-class and multinominal-class classification?
negative_passages.docid: 32548
negative_passages.text: On the importance of the i.i.d. assumption in statistical learning
negative_passages.docid: 47598
negative_passages.text: Motivation behind the definition of tangent vectors
negative_passages.docid: 126800
negative_passages.text: Why do we use multiple epochs and why does it not lead to over fitting
negative_passages.docid: 88139
negative_passages.text: Why binary crossentropy can be used as the loss function in autoencoders?
negative_passages.docid: 154757
negative_passages.text: One component in PCA is always the mean vector in two-dimensions but not three
negative_passages.docid: 60063
negative_passages.text: Independence between random vectors
negative_passages.docid: 114526
negative_passages.text: Question about vector-matirix derivative in back propagation
negative_passages.docid: 153261
negative_passages.text: Linear kernel and non-linear kernel for support vector machine?
negative_passages.docid: 73140
negative_passages.text: How does a Support Vector Machine (SVM) work?
negative_passages.docid: 112991
negative_passages.text: What algorithms need feature scaling, beside from SVM?
negative_passages.docid: 142667
negative_passages.text: Understanding how many biases are there in a neural network?