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- [[Category:Bayes' Classifier]]562 B (67 words) - 10:18, 29 April 2014
- ...s often used as an important tool to visualize the performance of a binary classifier. The use of ROC curves can be originated from signal detection theory that ...ping coins (heads or tails). As the size of the sample increases, a random classifier's ROC point migrates towards (0.5,0.5).11 KB (1,823 words) - 10:48, 22 January 2015
- ...eld of machine learning, one major topic is classification problem. Linear classifier is a class of algorithms that make the classification decision on a new tes There are mainly two classes of linear classifier: Generative model and Discriminative model:9 KB (1,540 words) - 10:56, 22 January 2015
- A classifier is given a set of <math> N </math> data-points (usually vectors) <math> x_{ ...se are often called the training set, because they are used to "train" the classifier.9 KB (1,604 words) - 10:54, 22 January 2015
- ...g. Adopting special metrics introduced previously, the robust and low-cost classifier could be set. However, users always have to be cautious on choosing invaria ...with the nearest neighbor classification will result in forming reasonable classifier.14 KB (2,323 words) - 04:54, 1 May 2014
- ...n the detailed example(Why useful) and finally an introduction of Bayesian classifier (more applications).1 KB (223 words) - 19:55, 3 May 2014
- ...g. Adopting special metrics introduced previously, the robust and low-cost classifier could be set. However, users always have to be cautious on choosing invaria ...with the nearest neighbor classification will result in forming reasonable classifier.14 KB (2,340 words) - 17:24, 12 May 2014
- ...s, you must implement the linear classifier yourself. (Do not use a linear classifier package; you must write your own code to find the normal vector to the hype2 KB (302 words) - 19:11, 31 March 2016
