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. New svms and kernels. Neural Networks. FastML. 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®.
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.