Machine Learning of Inductive Bias
Paul E. Utgoff
Π΄Π΅Ρ 2012. Β· The Springer International Series in Engineering and Computer Science 15. ΠΊΡΠΈΠ³Π° Β· Springer Science & Business Media
E-knjiga
166
Stranica
reportOcene i recenzije nisu verifikovane Β Saznajte viΕ‘e
O ovoj e-knjizi
This book is based on the author's Ph.D. dissertation[56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.
Ocenite ovu e-knjigu
Javite nam svoje miΕ‘ljenje.
Informacije o Δitanju
Pametni telefoni i tableti
Instalirajte aplikaciju Google Play knjige za Android i iPad/iPhone. Automatski se sinhronizuje sa nalogom i omogucΜava vam da Δitate onlajn i oflajn gde god da se nalazite.
Laptopovi i raΔunari
MoΕΎete da sluΕ‘ate audio-knjige kupljene na Google Play-u pomocΜu veb-pregledaΔa na raΔunaru.
E-ΔitaΔi i drugi ureΔaji
Da biste Δitali na ureΔajima koje koriste e-mastilo, kao Ε‘to su Kobo e-ΔitaΔi, treba da preuzmete fajl i prenesete ga na ureΔaj. Pratite detaljna uputstva iz centra za pomocΜ da biste preneli fajlove u podrΕΎane e-ΔitaΔe.
