query: libSVM for unbalanced data
positive_passages.docid: 156500
positive_passages.text: SVM for unbalanced data
negative_passages.docid: 13412
negative_passages.text: Motivating sigmoid output units in neural networks starting with unnormalized log probabilities linear in $z=w^Th+b$ and $\phi(z)$
negative_passages.docid: 36088
negative_passages.text: Class imbalance in Supervised Machine Learning
negative_passages.docid: 153261
negative_passages.text: Linear kernel and non-linear kernel for support vector machine?
negative_passages.docid: 18820
negative_passages.text: For imbalanced classification, should the validation dataset be balanced?
negative_passages.docid: 14138
negative_passages.text: Normality test with large data set
negative_passages.docid: 98863
negative_passages.text: Which metric to use to evaluate highly imbalance classification model performance
negative_passages.docid: 145064
negative_passages.text: Techniques to detect overfitting
negative_passages.docid: 40442
negative_passages.text: Is up- or down-sampling imbalanced data actually that effective? Why?
negative_passages.docid: 108996
negative_passages.text: Comparing and visualising highly skewed distributions
negative_passages.docid: 121045
negative_passages.text: Confusion on hinge loss and SVM
negative_passages.docid: 9663
negative_passages.text: Is it a good practice to always scale/normalize data for machine learning?
negative_passages.docid: 136197
negative_passages.text: Normalizing the final weights vector in the upper bound on the Perceptron's convergence
negative_passages.docid: 27388
negative_passages.text: scikit's RBM pseudo likelihood calculation
negative_passages.docid: 113807
negative_passages.text: Testing Classification on Oversampled Imbalance Data
negative_passages.docid: 12304
negative_passages.text: State-of-the-art algorithms for the training of neural networks with GRU or LSTM units
negative_passages.docid: 114711
negative_passages.text: Exact Sampling from Improper Mixtures