Estimating and Controlling the Uncertainty of Learning Machines

Anna Marconato; Andrea Boni; D. Petri

Estimating and Controlling the Uncertainty of Learning Machines

Anna Marconato, Andrea Boni, D. Petri

Electricity

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

2 Citations (Scopus)

Abstract

The problem of estimating model uncertainty of learning machines (LMs) is becoming a subject of great interest because of the wide application of such kind of methodologies for solving real-world problems. In this work we will provide a general overview on estimating and controlling uncertainity of LMs, by describing the algorithms, the theory and the empirical methods used to obtain a robust estimation. In the end we address the problem of uncertainty estimation when devices with limited resources are considered for the hardware implementation.

Original language	English
Title of host publication	AMUEM 2006 – IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, April 20-21, 2006, Trento, Italy
Pages	46-50
Number of pages	5
Publication status	Published - 20 Apr 2006
Event	Unknown - Duration: 20 Apr 2006 → …

Conference

Conference	Unknown
Period	20/04/06 → …

Keywords

model uncertainty
Support vector machines
model selection
smart sensors
genetic programming

Cite this

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title = "Estimating and Controlling the Uncertainty of Learning Machines",

abstract = "The problem of estimating model uncertainty of learning machines (LMs) is becoming a subject of great interest because of the wide application of such kind of methodologies for solving real-world problems. In this work we will provide a general overview on estimating and controlling uncertainity of LMs, by describing the algorithms, the theory and the empirical methods used to obtain a robust estimation. In the end we address the problem of uncertainty estimation when devices with limited resources are considered for the hardware implementation.",

keywords = "model uncertainty, Support vector machines, model selection, smart sensors, genetic programming",

author = "Anna Marconato and Andrea Boni and D. Petri",

year = "2006",

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note = "null ; Conference date: 20-04-2006",

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T1 - Estimating and Controlling the Uncertainty of Learning Machines

AU - Marconato, Anna

AU - Boni, Andrea

AU - Petri, D.

PY - 2006/4/20

Y1 - 2006/4/20

N2 - The problem of estimating model uncertainty of learning machines (LMs) is becoming a subject of great interest because of the wide application of such kind of methodologies for solving real-world problems. In this work we will provide a general overview on estimating and controlling uncertainity of LMs, by describing the algorithms, the theory and the empirical methods used to obtain a robust estimation. In the end we address the problem of uncertainty estimation when devices with limited resources are considered for the hardware implementation.

AB - The problem of estimating model uncertainty of learning machines (LMs) is becoming a subject of great interest because of the wide application of such kind of methodologies for solving real-world problems. In this work we will provide a general overview on estimating and controlling uncertainity of LMs, by describing the algorithms, the theory and the empirical methods used to obtain a robust estimation. In the end we address the problem of uncertainty estimation when devices with limited resources are considered for the hardware implementation.

KW - model uncertainty

KW - Support vector machines

KW - model selection

KW - smart sensors

KW - genetic programming

M3 - Conference paper

SN - 1-4244-0249-2

SP - 46

EP - 50

BT - AMUEM 2006 – IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, April 20-21, 2006, Trento, Italy

Y2 - 20 April 2006

ER -

Estimating and Controlling the Uncertainty of Learning Machines

Abstract

Conference

Keywords

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