enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    A training example of SVM with kernel given by φ((a, b)) = (a, b, a 2 + b 2) Suppose now that we would like to learn a nonlinear classification rule which corresponds to a linear classification rule for the transformed data points φ ( x i ) . {\displaystyle \varphi (\mathbf {x} _{i}).}

  3. Structured support vector machine - Wikipedia

    en.wikipedia.org/wiki/Structured_support_vector...

    Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels. As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree. Training a classifier consists of ...

  4. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]

  5. Discriminative model - Wikipedia

    en.wikipedia.org/wiki/Discriminative_model

    For example, in object recognition, is likely to be a vector of raw pixels (or features extracted from the raw pixels of the image). Within a probabilistic framework, this is done by modeling the conditional probability distribution P ( y | x ) {\displaystyle P(y|x)} , which can be used for predicting y {\displaystyle y} from x {\displaystyle x} .

  6. Sequential minimal optimization - Wikipedia

    en.wikipedia.org/wiki/Sequential_minimal...

    Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. [1] SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.

  7. Probabilistic classification - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_classification

    An example calibration plot. Calibration can be assessed using a calibration plot (also called a reliability diagram). [3] [5] A calibration plot shows the proportion of items in each class for bands of predicted probability or score (such as a distorted probability distribution or the "signed distance to the hyperplane" in a support vector ...

  8. Mystery man who spent 9 hours with photographer Hannah ...

    www.aol.com/mystery-man-spent-9-hours-091922959.html

    The man was one of the first people whom authorities tracked down after she was reported missing and police saw the pair on security cameras. “For a while, he was the last person to have seen ...

  9. Multiple instance learning - Wikipedia

    en.wikipedia.org/wiki/Multiple_Instance_Learning

    Given an image, an instance is taken to be one or more fixed-size subimages, and the bag of instances is taken to be the entire image. An image is labeled positive if it contains the target scene - a waterfall, for example - and negative otherwise.