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Zustand: BUONO USATO. RECLAMS UNIVERSAL BIBLIOTHEK TEDESCO Volume n. 1210 della Reclams Universal Bibliotek. Con un'introduzione di Ad. Stern. Brossura in cartoncino, formato tascabile, leggermente brunita dal tempo, con piccoli segni d'uso. Pagine ingiallite, come pure i tagli, salde, con ampio margine e note al piede. Tavola in bianco e nero all'antiporta. Numero pagine 69.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti c and commercial information. The need to analyze these masses of data has led to the evolution of the new eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the eld o ers the opportunity to combine the expertise of di erent elds intoacommonobjective.Moreover,withineach elddiversemethodshave been developed and justi ed with respect to di erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new eld of local patterns.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Es wurde gezeigt, daß das Bootstrap-Problem bei der geometrischen Szenenrekonstruktion eine wichtige Rolle spielt und daß das Problem der physikalischen Korrespondenz als eine spezielle Sichtweise des Rekonstruktionsproblems gesehen werden kann. Weiterhin wurde ein wissensbasier ter Ansatz vorgestellt, um das Bootstrap-Problem zu umgehen. Literatur Bajcsy + Lieberman 76 : Texture Gradient as a Depth Cue, R. Bajcsy und L.1. Lieberman, Computer Graphics and Image Processing 5, 52-67 (1976). Bartsch u.a. 86 : Merkmalsdetektion in Farbbildern als Grundlage zur Korrespondenzanalyse in Stereo-Bildfolgen, Thomas Bartsch, Leonie S. Dreschler-Fischer und Carsten Schröder, DAGM-86, pp. 94-97. Binford 81 : Inferring Surfaces /rom Images, Thomas O. Binford, Artificial Intelligence 17, 205-244 (1981) siehe auch: Brady 81, pp. 75-116. Blostein + Ahuja 87 : Representation and Three-Dimensional Interpretation of Image Texture: An Integrated Approach, Dorothea Blostein und Narendra Ahuja, ICCV-87, pp. 444-449. Brady 81 : Computer Vision, J.M. Brady (Rrsg.), North Holland Publ. Comp. Amsterdam 1981, reprinted /rom Artificial Intelligence 17 (1981). Clocksin 78 : Determining the Orientation of Surfaces /rom Optical Flow, W.F. Clocksin, Proc. AISB/GI-78 on Artificial Intelligence, Hamburg, July 18-20, 1978, pp. 73-102. Crowley 84a: A Computational Paradigm for Three Dimensional Scene Analysis, James 1. Crowley, Technical Report CMU-RI-TR-84-11 The Robotics Institute, Carnegie Mel10n University, Pittburgh, PA (April 1984). Dreschler + Nagel 82b : Volumetrie Model and 3D-Trajectory of a Moving Car Derived /rom Monocular TV Frame Sequences of aStreet Scene, L. Dreschler und H.-H. Nagel, Computer Graphics and Image Processing 20, 199--228 (1982).…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.…

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8°, Brosch. 237 S., mit 1 Farbabb., 11 farb. Grafiken, 4 s/w-Grafiken u. 2 Tab. Neuwertiges Ex. // Big Data, Data Science, maschinelles Lernen Begriffe, die auf Daten und ihren Wert für vielfältige Anwendungen hinweisen. Wem nützen die Daten? Wer darf sie nutzen? Wie werden wissenschaftlicher Fortschritt, Erfolg im ökonomischen Wettbewerb und Schutz der Privatsphäre gleichzeitig erreicht? Der Sammelband gibt die Referate einer Fachtagung der Nordrhein-Westfälischen Akademie der Wissenschaften und der Künste wieder, mit Beiträgen aus der Informatik, Statistik, Medizin, den Ingenieursdisziplinen, Rechts- und Wirtschaftswissenschaften. Damit beteiligt sich die Akademie an der dringend nötigen Diskussion, wie den Herausforderungen durch die heutigen Möglichkeiten der Datensammlung und -nutzung zu begegnen ist. ISBN: 9783506792488 Sprache: Deutsch Gewicht in Gramm: 361.…

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Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.…