supervised learning造句
1 Supervised learning is tasked with learning a function from labeled training data in order to predict the value of any valid input.
2 A new hybrid supervised learning control scheme is presented for continuous stirred tank reactor ( CSTR ) systems.
3 The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory.
4 The learning of connectionism, which consists mainly of supervised learning, intensive learning and unsupervised learning, is modelled after the learning of human beings.
5 The idea of supervised learning is to provide examples for an algorithm to learn from.
6 Text classification is a supervised learning task of assigning natural language text documents to one or more predefined categories or classes according to their contents.
7 The experiments, under supervised learning framework both isolated and joint, show significant gains of the coreference resolution system.
8 At the end of the supervised learning, students participate in a preceptorship and job search training, to help transition them from student to graduate.
9 The experiments, under supervised learning framework joint , show significant gains of the coreference resolution system.
10 A semi - supervised learning system was proposed based on ART ( adaptive resonance theory ).
11 The former belongs to supervised learning and the latter belongs to unsupervised learning.
12 Through semi - supervised learning,[www.] it uses simulated annealing method to get the minimized result.
13 Automatic medical image classification is to give the semantic category labels to medical images, which can be regarded as a supervised learning process.
14 Bayesian classification and the back of the linear, non - linear classifier belong to supervised learning.
15 The result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.
16 The author uses a instance about heart disease and explain the process of supervised learning.
17 Unsupervised learning is used to adjust input weight values and supervised learning is utilized to adjust output weight values.