Ranking Support Vector Machine with Kernel Approximation

<div>Margin-maximization for linear RankSVM. Four data points have the preference <svg height="10.5438pt" id="M32" style="vertical-align:-3.29108pt" version="1.1" viewbox="-0.0498162 -7.25272 89.8134 10.5438" width="89.8134pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M494 0V32C450 38 426 53 393 105L296 260C330 305 354 338 375 361C411 396 421 401 476 410V442H293V410C332 402 337 394 323 371C311 351 295 324 274 293L227 371C213 393 218 402 252 410V442H16V410C70 402 80 391 108 344L196 199C166 159 140 123 115 95C81 54 61 43 7 32V0H196V32C156 41 150 53 167 81C182 105 199 138 219 166L269 79C287 49 283 38 245 32V0H494Z" id="g13-118"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="482" vert-adv-y="482"></glyph.data></g><g transform="matrix(.0091,0,0,-0.0091,6.266,3.132)"><path d="M462 177V227H365V632H320C217 496 116 350 21 208V177H284V109C284 43 280 38 190 31V0H451V31C369 38 365 43 365 108V177H462ZM284 227H88C155 336 218 430 282 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Computational Intelligence and Neuroscience

Ranking Support Vector Machine with Kernel Approximation

Figure 1