Neural Comput - Extended robust support vector machine based on financial risk minimization.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
{ measur(2081) correl(1212) valu(896) }
{ risk(3053) factor(974) diseas(938) }
{ model(2341) predict(2261) use(1141) }
{ use(2086) technolog(871) perceiv(783) }
{ estim(2440) model(1874) function(577) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ model(3480) simul(1196) paramet(876) }
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{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ model(2220) cell(1177) simul(1124) }
{ search(2224) databas(1162) retriev(909) }
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{ sampl(1606) size(1419) use(1276) }
{ can(981) present(881) function(850) }
{ activ(1452) weight(1219) physic(1104) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Financial risk measures have been used recently in machine learning. For example, -support vector machine -SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than -SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

Resumo Limpo

financi risk measur use recent machin learn exampl support vector machin svm minim condit valu risk cvar margin distribut measur popular financ subaddit properti sensit outlier tail distribut propos new classif method extend robust svm ersvm minim intermedi risk measur cvar valu risk var expect result model becom less sensit svm outlier can regard ersvm extens robust svm use truncat hing loss numer experi impli ersvm possibl achiev better predict perform proper paramet set

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