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String kernel
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<p>In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> and <a href="/facts/Data_mining/AH53q5ac">data mining</a>, a string kernel is a <a href="/facts/Positive-definite_kernel/Ws3zkxfl">kernel function</a> that operates on <a href="/facts/String_(computer_science)/4ChJu5WS">strings</a>, i.e. finite sequences of symbols that need not be of the same length. String kernels can be intuitively understood as functions measuring the <a href="/facts/Similarity_measure/FmmFco8c">similarity</a> of pairs of strings: the more similar two strings <i>a</i> and <i>b</i> are, the higher the value of a string kernel <i>K</i>(<i>a</i>, <i>b</i>) will be. </p><p>Using string kernels with <a href="/facts/Kernel_trick/fYuURIPk">kernelized</a> learning algorithms such as <a href="/facts/Support_vector_machine/XobxpdBG">support vector machines</a> allow such algorithms to work with strings, without having to translate these to fixed-length, real-valued <a href="/facts/Feature_vector/nzAhYxfu">feature vectors</a>. String kernels are used in domains where sequence data are to be <a href="/facts/Cluster_analysis/HUeDnWPt">clustered</a> or <a href="/facts/Statistical_classification/jXXHRkXR">classified</a>, e.g. in <a href="/facts/Text_mining/5QsIa3Cz">text mining</a> and <a href="/facts/Bioinformatics/D5x2L8ee">gene analysis</a>. </p>