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12 matches in All Departments
- The author is one of the most influential AI reseachers of recent
decades. - Written in an accessible language, the book provides a
probing account of AI today and proposes a new narrative to connect
and make sense of events that happened in the recent tumultuous
past and enable us to think soberly about the road ahead. - The
book is divided into ten carefully crafted and easily-digestible
chapters, each grapples with an important question for AI, ranging
from the scientific concepts that underpin the technology to wider
implications for society, using real examples wherever possible.
- The author is one of the most influential AI reseachers of recent
decades. - Written in an accessible language, the book provides a
probing account of AI today and proposes a new narrative to connect
and make sense of events that happened in the recent tumultuous
past and enable us to think soberly about the road ahead. - The
book is divided into ten carefully crafted and easily-digestible
chapters, each grapples with an important question for AI, ranging
from the scientific concepts that underpin the technology to wider
implications for society, using real examples wherever possible.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I (Paperback, 2012 ed.)
Peter A. Flach, Tijl De Bie, Nello Cristianini
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R1,750
Discovery Miles 17 500
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Ships in 10 - 15 working days
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This two-volume set LNAI 7523 and LNAI 7524 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases: ECML PKDD 2012, held in
Bristol, UK, in September 2012. The 105 revised research papers
presented together with 5 invited talks were carefully reviewed and
selected from 443 submissions. The final sections of the
proceedings are devoted to Demo and Nectar papers. The Demo track
includes 10 papers (from 19 submissions) and the Nectar track
includes 4 papers (from 14 submissions). The papers grouped in
topical sections on association rules and frequent patterns;
Bayesian learning and graphical models; classification;
dimensionality reduction, feature selection and extraction;
distance-based methods and kernels; ensemble methods; graph and
tree mining; large-scale, distributed and parallel mining and
learning; multi-relational mining and learning; multi-task
learning; natural language processing; online learning and data
streams; privacy and security; rankings and recommendations;
reinforcement learning and planning; rule mining and subgroup
discovery; semi-supervised and transductive learning; sensor data;
sequence and string mining; social network mining; spatial and
geographical data mining; statistical methods and evaluation; time
series and temporal data mining; and transfer learning.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II (Paperback, 2012 ed.)
Peter A. Flach, Tijl De Bie, Nello Cristianini
|
R1,746
Discovery Miles 17 460
|
Ships in 10 - 15 working days
|
This two-volume set LNAI 7523 and LNAI 7524 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases: ECML PKDD 2012, held in
Bristol, UK, in September 2012. The 105 revised research papers
presented together with 5 invited talks were carefully reviewed and
selected from 443 submissions. The final sections of the
proceedings are devoted to Demo and Nectar papers. The Demo track
includes 10 papers (from 19 submissions) and the Nectar track
includes 4 papers (from 14 submissions). The papers grouped in
topical sections on association rules and frequent patterns;
Bayesian learning and graphical models; classification;
dimensionality reduction, feature selection and extraction;
distance-based methods and kernels; ensemble methods; graph and
tree mining; large-scale, distributed and parallel mining and
learning; multi-relational mining and learning; multi-task
learning; natural language processing; online learning and data
streams; privacy and security; rankings and recommendations;
reinforcement learning and planning; rule mining and subgroup
discovery; semi-supervised and transductive learning; sensor data;
sequence and string mining; social network mining; spatial and
geographical data mining; statistical methods and evaluation; time
series and temporal data mining; and transfer learning.
Nell'estate del 1914 l'Austria-Ungheria ha appena imboccato la
strada che la condurra alla catastrofe. Nella remota citta
turistica di Gorizia la mobilitazione inizia in agosto e coinvolge
anche la contessa Lucy Christalnigg, pilota di automobili in
missione per conto della Croce Rossa. Durante il viaggio tra
Klagenfurt e Gorizia, la contessa incontra un tragico destino. Dopo
100 anni questo racconto riporta alla luce un fatto di cronaca che
all'epoca aveva suscitato grande scalpore ma che fu subito
dimenticato a causa del disastroso conflitto. Questa e la storia
vera della prima vittima sull'Isonzo e degli ultimi giorni di un
mondo scomparso.
Where did SARS come from? Have we inherited genes from
Neanderthals? How do plants use their internal clock? The genomic
revolution in biology enables us to answer such questions. But the
revolution would have been impossible without the support of
powerful computational and statistical methods that enable us to
exploit genomic data. Many universities are introducing courses to
train the next generation of bioinformaticians: biologists fluent
in mathematics and computer science, and data analysts familiar
with biology. This readable and entertaining book, based on
successful taught courses, provides a roadmap to navigate entry to
this field. It guides the reader through key achievements of
bioinformatics, using a hands-on approach. Statistical sequence
analysis, sequence alignment, hidden Markov models, gene and motif
finding and more, are introduced in a rigorous yet accessible way.
A companion website provides the reader with Matlab-related
software tools for reproducing the steps demonstrated in the book.
Kernel methods provide a powerful and unified framework for pattern
discovery, motivating algorithms that can act on general types of
data (e.g. strings, vectors or text) and look for general types of
relations (e.g. rankings, classifications, regressions, clusters).
The application areas range from neural networks and pattern
recognition to machine learning and data mining. This book,
developed from lectures and tutorials, fulfils two major roles:
firstly it provides practitioners with a large toolkit of
algorithms, kernels and solutions ready to use for standard pattern
discovery problems in fields such as bioinformatics, text analysis,
image analysis. Secondly it provides an easy introduction for
students and researchers to the growing field of kernel-based
pattern analysis, demonstrating with examples how to handcraft an
algorithm or a kernel for a new specific application, and covering
all the necessary conceptual and mathematical tools to do so.
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
Where did HIV and SARS come from? Have we inherited genes
fromNeanderthals? How do plants use their internal clock? The
genomic revolution in biology enables us to answer such questions.
But the revolution would have been impossible without the support
of powerful computational and statistical methods that enable us to
exploit genomic data. Many universities are introducing courses to
train the next generation of bioinformaticians: biologists fluent
in mathematics and computer science, and data analysts familiar
with biology. This readable and entertaining book, based on
successful taught courses, provides a roadmap to navigate entry to
this field. It guides the reader through key achievements of
bioinformatics, using a hands-on approach. Statistical sequence
analysis, sequence alignment, hidden Markov models, gene and motif
finding and more, are introduced in a rigorous yet accessible way.
A companion website provides the reader with Matlab-related
software tools for reproducing the steps demonstrated in the book.
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