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38 relations: Artificial neural network, Backpropagation, Catastrophic interference, Computer science, Convex function, Empirical risk minimization, External memory algorithm, Feature hashing, Greedy algorithm, Hierarchical temporal memory, Hinge loss, Incremental learning, K-means clustering, K-nearest neighbors algorithm, Kernel method, Lazy learning, Learning vector quantization, Least squares, Loss function, Machine learning, Naive Bayes classifier, Offline learning, Online algorithm, Online optimization, Perceptron, Principal component analysis, Recursive least squares filter, Representer theorem, Scikit-learn, Stochastic gradient descent, Stochastic optimization, Stock market prediction, Streaming algorithm, Subderivative, Supervised learning, Support vector machine, Tikhonov regularization, Vowpal Wabbit.
Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Backpropagation
Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.
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Catastrophic interference
Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information.
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Computer science
Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations.
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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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Empirical risk minimization
Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on their performance.
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External memory algorithm
In computing, external memory algorithms or out-of-core algorithms are algorithms that are designed to process data that is too large to fit into a computer's main memory at one time.
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Feature hashing
In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix.
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Greedy algorithm
A greedy algorithm is an algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the intent of finding a global optimum.
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Hierarchical temporal memory
Hierarchical temporal memory (HTM) is a technology based on a realistic biologically-constrained model of the pyramidal neuron that reflects today’s most recent neocortical research originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee.
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Hinge loss
In machine learning, the hinge loss is a loss function used for training classifiers.
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Incremental learning
In computer science, incremental learning is a method of machine learning, in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model.
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K-means clustering
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.
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K-nearest neighbors algorithm
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.
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Kernel method
In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM).
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Lazy learning
In machine learning, lazy learning is a learning method in which generalization of the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries.
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Learning vector quantization
In computer science, learning vector quantization (LVQ), is a prototype-based supervised classification algorithm.
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Least squares
The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns.
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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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Naive Bayes classifier
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
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Offline learning
In machine learning, systems which employ offline learning do not change their approximation of the target function when the initial training phase has been completed.
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Online algorithm
In computer science, an online algorithm is one that can process its input piece-by-piece in a serial fashion, i.e., in the order that the input is fed to the algorithm, without having the entire input available from the start.
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Online optimization
Online optimization is a field of optimization theory, more popular in computer science and operations research, that deals with optimization problems having no or incomplete knowledge of the future (online).
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Perceptron
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).
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Principal component analysis
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
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Recursive least squares filter
Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals.
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Representer theorem
In statistical learning theory, a representer theorem is any of several related results stating that a minimizer f^ of a regularized empirical risk function defined over a reproducing kernel Hilbert space can be represented as a finite linear combination of kernel products evaluated on the input points in the training set data.
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Scikit-learn
Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.
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Stochastic gradient descent
Stochastic gradient descent (often shortened to SGD), also known as incremental gradient descent, is an iterative method for optimizing a differentiable objective function, a stochastic approximation of gradient descent optimization.
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Stochastic optimization
Stochastic optimization (SO) methods are optimization methods that generate and use random variables.
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Stock market prediction
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange.
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Streaming algorithm
In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes (typically just one).
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Subderivative
In mathematics, the subderivative, subgradient, and subdifferential generalize the derivative to functions which are not differentiable.
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Supervised learning
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
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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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Tikhonov regularization
Tikhonov regularization, named for Andrey Tikhonov, is the most commonly used method of regularization of ill-posed problems.
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Vowpal Wabbit
Vowpal Wabbit (also known as "VW") is an open source fast out-of-core machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research.
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Batch learning, On-line learning, Online Machine Learning, Online convex optimization, Online learning model.
References
[1] https://en.wikipedia.org/wiki/Online_machine_learning
