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  2. Verification and validation of computer simulation models

    en.wikipedia.org/wiki/Verification_and...

    Validation checks the accuracy of the model's representation of the real system. Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model". [3]

  3. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    The fitted model is evaluated using “new” examples from the held-out data sets (validation and test data sets) to estimate the model’s accuracy in classifying new data. [5] To reduce the risk of issues such as over-fitting, the examples in the validation and test data sets should not be used to train the model. [5]

  4. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    GE Healthcare used TensorFlow to increase the speed and accuracy of MRIs in identifying specific body parts. [74] Google used TensorFlow to create DermAssist, a free mobile application that allows users to take pictures of their skin and identify potential health complications. [75]

  5. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    SqueezeNet was originally described in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [1] AlexNet is a deep neural network that has 240 MB of parameters, and SqueezeNet has just 5 MB of parameters. This small model size can more easily fit into computer memory and can more easily be transmitted over a ...

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.

  7. CIFAR-10 - Wikipedia

    en.wikipedia.org/wiki/CIFAR-10

    The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research.

  8. Tensor (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Tensor_(machine_learning)

    In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...

  9. Test functions for optimization - Wikipedia

    en.wikipedia.org/wiki/Test_functions_for...

    The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and Binh. [6] The software developed by Deb can be downloaded, [ 7 ] which implements the NSGA-II procedure with GAs, or the program posted on Internet, [ 8 ] which implements the NSGA-II procedure with ES.