Realms of Meaning presents an accessible introduction to semantics. It provides an understanding of the way meaning works in natural languages, against a background of how we communicate with language. Avoiding theoretical terminology and linguistic theories it concentrates instead on the analysis of meaning, and looks in depth at such subjects as opposites and negatives, modal verbs, prepositions and word meanings. Examples are chosen mainly from English to provide material for the wider discussion of the principles of the subject, but European, East Asian and other languages also provide illuminating examples.

This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

Die Lehrbuchreihe Bankgeschafte fiir den Praktiker ist fUr Bankkaufleute ge macht, die sich in eine neues Fachgebiet einarbeiten wollen. Die Autoren gehen deshalb bei der Gestaltung dieses Fachbuches yom Wissensstand eines Mitarbei ters im Bankbetrieb aus, der im Rahmen seiner Ausbildung Grundkenntnisse zu dem jeweiligen Themenbereich erworben hat und nunmehr vor die Aufgabe ge stellt ist, als qualifizierter Sachbearbeiter ein bestimmtes Gebiet im Bankge schaft abzudecken. Aus dies em Grunde eignet sich das Buch auch fUr Teilneh mer an BankfortbildungsmaBnahmen. Damit ist diese Lehrbuchreihe zwischen der Ausbildungsliteratur und der Hochschulliteratur angesiedelt. Die Lehrbticher werden betont praxisnah ge staltet, indem jeweils von konkreten Fallbeispielen ausgegangen wird, die im Bankgeschaft auftreten. 1m Rahmen dieser Fallsituation werden die not wend i gen Erlauterungen zum Sachverhalt in anschaulicher und strukturierter Form dargeboten. Die rechtlichen Probleme und wirtschaftlichen Risiken, die mit dem Grund buch zusammenhangen, sind vielschichtiger Art. Deshalb werden die typischen Problemfalle nicht nur unter juristischen Fragestellungen - unter Mitarbeit ei nes Notars - sondern auch in der kaufmannischen Bewertung aus der Sicht der Bank dargestellt. In die Beispiele werden aIle notwendigen Formulare, Textbei spiele und Ablaufschemata eingefUgt, die fUr das Verstandnis und die Nachvoll ziehbarkeit der FaIle notwendig sind. Wesentliche Begriffe werden in einem angeftigten Stichwortverzeichnis er lautert, so daB dieses Werk nicht nur als Lehrbuch sondern auch als Nachschla gewerk einsetzbar ist. Unser Dank gilt besonders all denjenigen, die uns bereitwillig geholfen ha ben, das "Grundbuch im Kreditgeschaft" aktuell und praxisnah zu gestalten."

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field. Contributors Yasemin Altun, Goekhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daume III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Perez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schoelkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston

Thomas Hofmannuntersucht die Bedeutung des Preises sowie weiterer Einflussfaktoren auf die wahrgenommene Qualitat am Beispiel von verschiedenen Produkten und in drei Kulturen. Es zeigt sich, dass Preise uber die betrachteten Produkte und Kulturen hinweg als Qualitatsindikatoren herangezogen werden. Im Gegensatz dazu ist die Bedeutung z.B. von emotionalen oder sozialen Faktoren sowohl produkt- als auch kulturabhangig. "