1.

Record Nr.

UNINA9910451061203321

Titolo

Interpreting the maternal organisation [[electronic resource] /] / edited by Heather Höpfl and Monika Kostera

Pubbl/distr/stampa

London ; ; New York, : Routledge, 2003

ISBN

0-203-21655-5

1-134-45021-4

1-280-51773-5

Descrizione fisica

1 online resource (264 p.)

Collana

Routledge Studies in Human Resource Development

Altri autori (Persone)

HöpflHeather

KosteraMonika <1963->

Disciplina

302.3/5

Soggetti

Organization - Philosophy

Organizational sociology

Organizational behavior

Sex role in the work environment

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Book Cover; Title; Copyright; Contents

Sommario/riassunto

Over the past ten to fifteen years there has been an increasing interest in emotion in organizations, in diversity, ethics, care and the ubiquitous pursuit of quality. These concerns, however, have consistently been reduced to issues of management and regulation. There is now a growing need to confront issues related to the dehumanization of organizations. This book brings these issues together, presenting an original construction of the organization via an emphasis on the (m)other.This book is not a feminist tract, nor is it primarily about the experiences of women in organizations. It ra



2.

Record Nr.

UNISALENTO991003049529707536

Autore

Cino : da Pistoia

Titolo

Le Rime / di messer Cino da Pistoia ; ridotte a miglior lezione da Enrico Bindi e Pietro Fanfani

Pubbl/distr/stampa

Pistoia : Tip. Niccolai, 1878

Descrizione fisica

CI, 447 p. ; 19 cm

Altri autori (Persone)

Fanfani, Pietro

Bindi, Enrico

Disciplina

851.1

Soggetti

Cino : da Pistoia - Opere poetiche

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

3.

Record Nr.

UNINA9910144348503321

Titolo

Advanced Lectures on Machine Learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures / / edited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch

Pubbl/distr/stampa

Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004

ISBN

3-540-28650-0

Edizione

[1st ed. 2004.]

Descrizione fisica

1 online resource (X, 246 p.)

Collana

Lecture Notes in Artificial Intelligence ; ; 3176

Disciplina

006.3

Soggetti

Artificial intelligence

Computer science

Algorithms

Computers

Pattern perception

Artificial Intelligence

Computer Science, general

Algorithm Analysis and Problem Complexity

Computation by Abstract Devices

Pattern Recognition



Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

An Introduction to Pattern Classification -- Some Notes on Applied Mathematics for Machine Learning -- Bayesian Inference: An Introduction to Principles and Practice in Machine Learning -- Gaussian Processes in Machine Learning -- Unsupervised Learning -- Monte Carlo Methods for Absolute Beginners -- Stochastic Learning -- to Statistical Learning Theory -- Concentration Inequalities.

Sommario/riassunto

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.