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Structured Support Vector Machine
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. As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree. Training a classifier consists of showing pairs of correct sample and output label pairs. After training, the structured SVM model allows one to predict for new sample instances the corresponding output label; that is, given a natural language sentence, the classifier can produce the most likely parse tree. Training For a set of n training instances (\boldsymbol_i,y_i) \in \mathcal\times\mathcal, i=1,\dots,n from a sample space \mathcal and label space \mathcal, the structured SVM minimizes the following regularized risk function. :\underset ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Support-vector Machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning, supervised Maximum-margin hyperplane, max-margin models with associated learning algorithms that analyze data for Statistical classification, classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vladimir Vapnik, Vapnik (1982, 1995) and Alexey Chervonenkis, Chervonenkis (1974). In addition to performing linear classifier, linear classification, SVMs can efficiently perform non-linear classification using the Kernel method#Mathematics: the kernel trick, ''kernel trick'', representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function, which transforms them into coordinates in a higher-dimensional feature space. Thus, SVMs use the kernel trick to implicitly map their inputs int ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
