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| example [2015/03/04 11:30] – epsatisep | example [2019/04/07 13:32] (current) – [MathJax Plugin Usage Example] team2 | ||
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| ==== Refnotes Plugin Usage Examples ==== | ==== Refnotes Plugin Usage Examples ==== | ||
| - | This is a reference to a book [(bandyopadhyay2013unsupervised)], | + | This is a reference to a book [(Mulder2013428)] |
| + | |||
| + | Please use superscript and subscript annotations to write powers and indexes, //e.g.//, m< | ||
| ==== Caption Plugin Usage Examples ==== | ==== Caption Plugin Usage Examples ==== | ||
| Line 10: | Line 12: | ||
| Table {{ref> | Table {{ref> | ||
| - | < | + | < |
| <table tlabel0> | <table tlabel0> | ||
| < | < | ||
| + | <WRAP box 400px center> | ||
| ^ Abbreviation ^ Description ^ | ^ Abbreviation ^ Description ^ | ||
| |EPS |European Project Semester| | |EPS |European Project Semester| | ||
| |ISEP|Instituto Superior de Engenharia do Porto| | |ISEP|Instituto Superior de Engenharia do Porto| | ||
| |USB |Universal Serial Bus| | |USB |Universal Serial Bus| | ||
| + | </ | ||
| </ | </ | ||
| </ | </ | ||
| Table {{ref> | Table {{ref> | ||
| - | |||
| - | <WRAP centeralign> | ||
| <table tlabel1> | <table tlabel1> | ||
| < | < | ||
| - | ^ API ^ Time (s) ^ | + | <WRAP 120px> |
| + | ^ API ^ Time (s) ^ | ||
| | EJML | | EJML | ||
| | JAMA | | JAMA | ||
| Line 31: | Line 34: | ||
| | MTJ | 18 | | MTJ | 18 | ||
| | WEKA | | WEKA | ||
| - | </ | ||
| </ | </ | ||
| + | </ | ||
| + | |||
| <WRAP centeralign> | <WRAP centeralign> | ||
| <table tlabel2> | <table tlabel2> | ||
| Line 59: | Line 63: | ||
| Equation \ref{eq: | Equation \ref{eq: | ||
| + | This is an in line expression $a+b=c$. | ||
| <WRAP centeralign> | <WRAP centeralign> | ||
| Line 65: | Line 70: | ||
| | | ||
| \end{equation} | \end{equation} | ||
| - | </ | + | </ |
| As [(bandyopadhyay2013unsupervised)] states, the most popular similarity metrics are the distance and the cosine similarity. The distance-based metrics include the Euclidean distance, the Hamming distance or the Chebyshev distance, among others. | As [(bandyopadhyay2013unsupervised)] states, the most popular similarity metrics are the distance and the cosine similarity. The distance-based metrics include the Euclidean distance, the Hamming distance or the Chebyshev distance, among others. | ||
