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15 relations: Convex function, Differentiable function, Hamming distance, Hyperplane, International Joint Conference on Artificial Intelligence, Journal of Machine Learning Research, Lecture Notes in Computer Science, Loss function, Machine learning, Multiclass classification, Smoothness, Statistical classification, Structured prediction, Structured support vector machine, Support vector machine.
Convex function
In mathematics, a real-valued function defined on an ''n''-dimensional interval is called convex (or convex downward or concave upward) if the line segment between any two points on the graph of the function lies above or on the graph, in a Euclidean space (or more generally a vector space) of at least two dimensions.
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Differentiable function
In calculus (a branch of mathematics), a differentiable function of one real variable is a function whose derivative exists at each point in its domain.
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Hamming distance
In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.
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Hyperplane
In geometry, a hyperplane is a subspace whose dimension is one less than that of its ambient space.
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International Joint Conference on Artificial Intelligence
The International Joint Conference on Artificial Intelligence (IJCAI) is a gathering of artificial intelligence researchers and practitioners.
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Journal of Machine Learning Research
The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning.
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Lecture Notes in Computer Science
Springer Lecture Notes in Computer Science (LNCS) is a series of computer science books published by Springer Science+Business Media (formerly Springer-Verlag) since 1973.
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Loss function
In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
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Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
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Multiclass classification
Not to be confused with multi-label classification. In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes.
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Smoothness
In mathematical analysis, the smoothness of a function is a property measured by the number of derivatives it has that are continuous.
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Statistical classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
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Structured prediction
Structured prediction or structured (output) learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than scalar discrete or real values.
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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.
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Support vector machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
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