enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Normalization (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(machine...

    Adaptive instance normalization (AdaIN) is a variant of instance normalization, designed specifically for neural style transfer with CNNs, rather than just CNNs in general. [ 27 ] In the AdaIN method of style transfer, we take a CNN and two input images, one for content and one for style .

  3. Batch normalization - Wikipedia

    en.wikipedia.org/wiki/Batch_normalization

    In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.

  4. Instance-based learning - Wikipedia

    en.wikipedia.org/wiki/Instance-based_learning

    In machine learning, instance-based learning (sometimes called memory-based learning [1]) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed ...

  5. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.

  6. Multiple instance learning - Wikipedia

    en.wikipedia.org/wiki/Multiple_Instance_Learning

    The first step tries to learn instance-level concepts by building a decision tree from each instance in each bag of the training set. Each bag is then mapped to a feature vector based on the counts in the decision tree. In the second step, a single-instance algorithm is run on the feature vectors to learn the concept

  7. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    The norm (see also Norms) can be used to approximate the optimal norm via convex relaxation. It can be shown that the L 1 {\displaystyle L_{1}} norm induces sparsity. In the case of least squares, this problem is known as LASSO in statistics and basis pursuit in signal processing.

  8. Batch processing - Wikipedia

    en.wikipedia.org/wiki/Batch_processing

    A batch window is "a period of less-intensive online activity", [11] when the computer system is able to run batch jobs without interference from, or with, interactive online systems. A bank's end-of-day (EOD) jobs require the concept of cutover , where transaction and data are cut off for a particular day's batch activity ("deposits after 3 PM ...

  9. Minimum mean square error - Wikipedia

    en.wikipedia.org/wiki/Minimum_mean_square_error

    Standard method like Gauss elimination can be used to solve the matrix equation for .A more numerically stable method is provided by QR decomposition method. Since the matrix is a symmetric positive definite matrix, can be solved twice as fast with the Cholesky decomposition, while for large sparse systems conjugate gradient method is more effective.