Katharina morik (197 resultados)

Broschiert. Condición: Gut. 269 Seiten; Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). Sprache: Deutsch Gewicht in Gramm: 535.

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Paperback. Condición: Very Good. Sorry, No international shipping on this item.Ex-library paperback in very nice condition with the usual markings and attachments. Text block clean and unmarked. Tight binding.

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Paperback or Softback. Condición: New. Gwai-87 11th German Workshop on Artificial Intelligence: Geseke, September 28-October 2, 1987 Proceedings. Book.

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Condición: New. In English.

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Condición: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail.…

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Paperback. Condición: new. Paperback. Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning. Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is e Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: New. Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.…

*kart. Condición: Sehr gut. X, 405 S. : graph. Darst. , 716 g , 2 cm - eher wenige Spuren, gut bis altersgemäß sehr gut, unkenntlich gemachter Namensaufkleber (siehe auch mein Foto) [sw.] --- bitte beachten Sie auch meine weiteren Angebote an Büchern aus der Reihe "Informatik Fachberichte" erschienen bei Springer si Sprache: Englisch Gewicht in Gramm: 716. …

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Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condición: New. pp. 248.

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Condición: New. pp. 420.

Paperback. Condición: Brand New. bilingual edition. 420 pages. German language. 9.60x6.69x0.86 inches. In Stock.

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Taschenbuch. Condición: 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. Condición: 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. Condición: 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.…