IEEE Trans Pattern Anal Mach Intell - Linear Latent Force Models Using Gaussian Processes.

Tópicos

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{ data(3963) clinic(1234) research(1004) }
{ model(2656) set(1616) predict(1553) }
{ use(1733) differ(960) four(931) }
{ learn(2355) train(1041) set(1003) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ model(3480) simul(1196) paramet(876) }
{ implement(1333) system(1263) develop(1122) }
{ framework(1458) process(801) describ(734) }
{ estim(2440) model(1874) function(577) }
{ model(3404) distribut(989) bayesian(671) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ general(901) number(790) one(736) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ perform(1367) use(1326) method(1137) }
{ monitor(1329) mobil(1314) devic(1160) }
{ gene(2352) biolog(1181) express(1162) }
{ high(1669) rate(1365) level(1280) }
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{ activ(1452) weight(1219) physic(1104) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
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{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. We show how different, physically-inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology and geostatistics.

Resumo Limpo

pure data driven approach machin learn present difficulti data scarc relat complex model model forc extrapol hand pure mechanist approach need identifi specifi interact problem hand may feasibl still leav issu parameter system paper present hybrid approach use gaussian process differenti equat combin data driven model physic model system show differ physicallyinspir kernel function can develop sensibl simpl mechanist assumpt under system versatil approach illustr three case studi motion captur comput biolog geostatist

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