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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Online machine learning ： ウィキペディア英語版 Online machine learning Online machine learning is used when data becomes available in a sequential order to determine a mapping from data set corresponding labels. The difference between online learning and batch learning (or "offline" learning) techniques, is that in online learning the mapping is updated after the arrival of every new data point in a scale fashion, whereas batch techniques are used when one has access to the entire training data set at once. Online learning could be used in the case of a process occurring in time, for example the value of a stock given its history and other external factors, in which case the mapping updates as time goes on and we get more and more samples. Ideally in online learning, the memory needed to store the function remains constant even with added data points, since the solution computed at one step is updated when a new data point becomes available, after which that data point can then be discarded. For many formulations, for example nonlinear kernel methods, true online learning is not possible, though a form of hybrid online learning with recursive algorithms can be used. In this case, the space requirements are no longer guaranteed to be constant since it requires storing all previous data points, but the solution may take less time to compute with the addition of a new data point, as compared to batch learning techniques. As in all machine learning problems, the goal of the algorithm is to minimize some performance criteria using a loss function. For example, with stock market prediction the algorithm may attempt to minimize the mean squared error between the predicted and true value of a stock. Another popular performance criterion is to minimize the number of mistakes when dealing with classification problems. In addition to applications of a sequential nature, online learning algorithms are also relevant in applications with huge amounts of data such that traditional learning approaches that use the entire data set in aggregate are computationally infeasible. ==A prototypical online supervised learning algorithm== In the setting of supervised learning, or learning from examples, we are interested in learning a function $f\; :\; X\; \backslash to\; Y$, where $X$ is thought of as a space of inputs and $Y$ as a space of outputs, that predicts well on instances that are drawn from a joint probability distribution $p(x,y)$ on $X\; \backslash times\; Y$. In this setting, we are given a loss function $V\; :\; Y\; \backslash times\; Y\; \backslash to\; \backslash mathbb$, such that $V(f(x),\; y)$ measures the difference between the predicted value $f(x)$ and the true value $y$. The ideal goal is to select a function $f\; \backslash in\; \backslash mathcal$, where $\backslash mathcal$ is a space of functions called a hypothesis space, so as to minimize the expected risk: : $I()\; =\; \backslash mathbb(y))\; =\; \backslash int\; V(f(x),\; y)\backslash ,dp(x,\; y)\; \backslash \; .$ In reality, the learner never knows the true distribution $p(x,y)$ over instances. Instead, the learner usually has access to a training set of examples $(x\_1,\; y\_1),\; \backslash ldots,\; (x\_n,\; y\_n)$ that are assumed to have been drawn i.i.d. from the true distribution $p(x,y)$. A common paradigm in this situation is to estimate a function $\backslash hat$ through empirical risk minimization or regularized empirical risk minimization (usually Tikhonov regularization). The choice of loss function here gives rise to several well-known learning algorithms such as regularized least squares and support vector machines. The above paradigm is not well-suited to the online learning setting though, as it requires complete a priori knowledge of the entire training set. In the pure online learning approach, the learning algorithm should update a sequence of functions $f\_1,\; f\_2,\; \backslash ldots$ in a way such that the function $f\_$ depends only on the previous function $f\_t$ and the next data point $(x\_t,\; y\_t)$. This approach has low memory requirements in the sense that it only requires storage of a representation of the current function $f\_t$ and the next data point $(x\_t,\; y\_t)$. A related approach that has larger memory requirements allows $f\_$ to depend on $f\_t$ and all previous data points $(x\_1,\; y\_1),\; \backslash ldots,\; (x\_t,\; y\_t)$. We focus solely on the former approach here, and we consider both the case where the data is coming from an infinite stream $(x\_1,\; y\_1),\; (x\_2,\; y\_2),\; \backslash ldots$ and the case where the data is coming from a finite training set $(x\_1,\; y\_1),\; \backslash ldots,\; (x\_n,\; y\_n)$, in which case the online learning algorithm may make multiple passes through the data. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Online machine learning」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.