Amanote Research

Amanote Research

    RegisterSign In

How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars

Combinatorial Chemistry and High Throughput Screening - United Arab Emirates
doi 10.2174/138620709788489064
Full Text
Open PDF
Abstract

Available in full text

Categories
MedicineOrganic ChemistryDrug DiscoveryComputer Science Applications
Date

June 1, 2009

Authors
Anton SchwaighoferTimon SchroeterSebastian MikaGilles Blanchard
Publisher

Bentham Science Publishers Ltd.


Related search

Can We Get Happier Than We Are?

2011English

Weather Oddities (Or How People Get It Wrong)

Weather
Atmospheric Science
2017English

Can Machine Learning Approaches Lead Toward Personalized Cognitive Training?

Frontiers in Behavioral Neuroscience
Cognitive NeurosciencePhysiological PsychologyBehavioral NeuroscienceNeuropsychology
2019English

More on the Bristol Affair: What Went Wrong and How Can We Move Forward?

BMJ
1999English

Can We Get There From Here?

Wilderness and Environmental Medicine
EnvironmentalPublic HealthOccupational HealthSports ScienceEmergency Medicine
2015English

Can a Machine Understand Real Estate Pricing? – Evaluating Machine Learning Approaches With Big Data

2019English

Can We All Just Get Along?

Nature Genetics
Genetics
2012English

Machine Learning Approaches to Study HIV/AIDS Infection: A Review

Bioscience Biotechnology Research Communications
2017English

Delirious Mania: Can We Get Away With This Concept? A Case Report and Review of the Literature

Case Reports in Psychiatry
PsychiatryMental Health
2012English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy