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Kernel classifiers were described as early as the 1960s, with the invention of the kernel perceptron. [3] They rose to great prominence with the popularity of the support-vector machine (SVM) in the 1990s, when the SVM was found to be competitive with neural networks on tasks such as handwriting recognition.
Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
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 ...
In 2011, the company started publishing its hosted service for the mxGraph web application under a separate brand, Diagramly with the domain "diagram.ly". [12]After removing the remaining use of Java applets from its web app, the service rebranded as draw.io in 2012 because the ".io suffix is a lot cooler than .ly", said co-founder David Benson in a 2012 interview.
Memory-mapped I/O is preferred in IA-32 and x86-64 based architectures because the instructions that perform port-based I/O are limited to one register: EAX, AX, and AL are the only registers that data can be moved into or out of, and either a byte-sized immediate value in the instruction or a value in register DX determines which port is the source or destination port of the transfer.
In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables, allowing learning of non-linear models.
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 .
Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate the properties of functions in a computationally efficient way and allow algorithms to easily swap functions of varying complexity.