Multiclass classification

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies. Multiclass classification should not be confused with multi-label classification, where multiple labels are to be predicted for each instance. The existing multi-class classification techniques can be categorised into transformation to binary extension from binary hierarchical classification. This section discusses strategies for reducing the problem of multiclass classification to multiple binary classification problems. It can be categorized into one vs rest and one vs one. The techniques developed based on reducing the multi-class problem into multiple binary problems can also be called problem transformation techniques. One-vs.-rest (OvR or one-vs.-all, OvA or one-against-all, OAA) strategy involves training a single classifier per class, with the samples of that class as positive samples and all other samples as negatives. This strategy requires the base classifiers to produce a real-valued confidence score for its decision, rather than just a class label; discrete class labels alone can lead to ambiguities, where multiple classes are predicted for a single sample. In pseudocode, the training algorithm for an OvR learner constructed from a binary classification learner L is as follows: Inputs: L, a learner (training algorithm for binary classifiers) samples X labels y where y_i ∈ {1, ... K} is the label for the sample X_i Output: a list of classifiers f_k for k ∈ {1, ..., K} Procedure: For each k in {1, ...

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation