Machine Learning Training in Egypt | NobleProg Middle East.
Compete. Books. Courses. 1.4. Support Vector Machines. The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64. 1.4.1. Classification SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset. SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations (see section Mathematical formulation).
As other classifiers, SVC, NuSVC and LinearSVC take as input two arrays: an array X of size [n_samples,n_features] holding the training samples, and an array y of class labels (strings or integers), size [n_samples]: After being fitted, the model can then be used to predict new values: >>> clf.predict([[2., 2.]])array([1]) . New svms and kernels.
FastML. The Clever Machine | Topics in Computational Neuroscience & Machine Learning. Machine Learning Mastery. MATLAB Documentation. The Language of Technical Computing MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java®. You can use MATLAB for a range of applications, including signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology. More than a million engineers and scientists in industry and academia use MATLAB, the language of technical computing.
Language Fundamentals Syntax, operators, data types, array indexing and manipulation Mathematics Linear algebra, basic statistics, differentiation and integrals, Fourier transforms, and other mathematics Graphics. Cocktail party demo. Imagine you're at a cocktail party. For you it is no problem to follow the discussion of your neighbours, even if there are lots of other sound sources in the room: other discussions in English and in other languages, different kinds of music, etc..
You might even hear a siren from the passing-by police car. It is not known exactly how humans are able to separate the different sound sources. Independent component analysis is able to do it, if there are at least as many microphones or 'ears' in the room as there are different simultaneous sound sources. In this demo, you can select which sounds are present in your cocktail party.
ICA will separate them without knowing anything about the different sound sources or the positions of the microphones. Select the sound sources you wish by clicking the boxes under the icons. ICA Research at Helsinki University of Technology.