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This can be seen as a structured prediction problem [2] in which the structured output domain is the set of all possible parse trees. Structured prediction is used in a wide variety of domains including bioinformatics, natural language processing (NLP), speech recognition, and computer vision.
The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification , multiclass classification and regression , the structured SVM allows training of a classifier for general structured output labels .
Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as a human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data to expected output values. [1]
Structured support-vector machine is an extension of the traditional SVM model. While the SVM model is primarily designed for binary classification, multiclass classification, and regression tasks, structured SVM broadens its application to handle general structured output labels, for example parse trees, classification with taxonomies ...
A structured general-purpose dataset on life, work, and death of 1.22 million distinguished people. Public domain. A five-step method to infer birth and death years, gender, and occupation from community-submitted data to all language versions of the Wikipedia project. 1,223,009 Text Regression, Classification 2022 Paper [258] Dataset [259]
The ML "model" includes a specification of a pdf, which in this case is the pdf of the unknown source signals . Using ML ICA , the objective is to find an unmixing matrix that yields extracted signals y = W x {\displaystyle y=\mathbf {W} x} with a joint pdf as similar as possible to the joint pdf p s {\displaystyle p_{s}} of the unknown source ...
In structured prediction, the hinge loss can be further extended to structured output spaces. Structured SVMs with margin rescaling use the following variant, where w denotes the SVM's parameters, y the SVM's predictions, φ the joint feature function, and Δ the Hamming loss:
Dictionary size of input & output languages respectively. x, Y: 9k and 10k 1-hot dictionary vectors. x → x implemented as a lookup table rather than vector multiplication. Y is the 1-hot maximizer of the linear Decoder layer D; that is, it takes the argmax of D's linear layer output. x 300-long word embedding vector.