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In case of a crisis companies are recommended to follow a one-voice-policy in the communication with their stakeholders. The following chapter investigates how the one-voice-policy is performed in multilingual business writing. In doing so it will be shown which linguistic means are sensitive for translating the original in the target language and how they may violate the principle of one-voice-policy.

Multilingualism --- Language acquisition --- Languages & Literatures --- Philology & Linguistics --- Multilingualism. --- Language acquisition. --- Acquisition of language --- Developmental linguistics --- Developmental psycholinguistics --- Language and languages --- Language development in children --- Psycholinguistics, Developmental --- Plurilingualism --- Polyglottism --- Acquisition --- Interpersonal communication in children --- Psycholinguistics --- E-books

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This book is about categorization processes in native/non-native workplace interaction, within the context of internship interviews between Danish employers and second language speakers who were born abroad. In this volume, which is one of the first books on gatekeeping, Tranekjær seeks to address processes of power and ideology from a conversation analytical perspective. The book examines the challenges that non-native internship candidates face in processes of employment when employers and job-counsellors seek to conceptualize, categorize and address the candidates’ linguistic, ethnic and religious otherness. The book shows how processes of categorization are influenced by broader structures of ideology related to social issues of controversy and debate such as migration, integration and second-language learning. The book also includes an overview of previous gatekeeping studies and proposes a redefinition of the term, which suggests a broader meaning and relevance of the notion.

Categorization (Linguistics) --- Conversation analysis --- Sociolinguistics --- Language acquisition --- Acquisition of language --- Developmental linguistics --- Developmental psycholinguistics --- Language and languages --- Language development in children --- Psycholinguistics, Developmental --- Interpersonal communication in children --- Psycholinguistics --- Language and society --- Society and language --- Sociology of language --- Language and culture --- Linguistics --- Sociology --- Integrational linguistics (Oxford school) --- Analysis of conversation --- CA (Interpersonal communication) --- Conversational analysis --- Oral communication --- Classification (Linguistics) --- Linguistic analysis (Linguistics) --- Acquisition --- Social aspects --- Sociological aspects --- E-books --- Conversation analysis. --- Sociolinguistics. --- Language acquisition. --- Applied Conversation Analysis. --- Cultural Studies. --- Discursive Psychology. --- Interactional sociolinguistics. --- Intercultural communication. --- Membership Categorization Analysis. --- Power. --- Second language interaction. --- Social categories. --- Work-place communication. --- Work-place studies. --- categorization processes. --- employment. --- ethnic otherness. --- gatekeeping. --- linguistic otherness. --- religious otherness.

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Complex analysis --- Artificial intelligence. Robotics. Simulation. Graphics --- Algorithms --- Machine Learning --- Machine learning --- Kernel functions --- Engineering & Applied Sciences --- Computer Science --- Algorithms. --- Kernel functions. --- Machine learning. --- 519.213 --- #TELE:SISTA --- 681.3*I26 --- Learning, Machine --- Functions, Kernel --- Algorism --- 519.213 Probability distributions and densities. Normal distribution. Characteristic functions. Measures of dependence. Infinitely divisible laws. Stable laws --- Probability distributions and densities. Normal distribution. Characteristic functions. Measures of dependence. Infinitely divisible laws. Stable laws --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Artificial intelligence --- Machine theory --- Functions of complex variables --- Geometric function theory --- Algebra --- Arithmetic --- Foundations --- E-books

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Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; New chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material. * Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques * Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods * Performance improvement techniques that work by transforming the input or output * Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive Interface.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Data mining. --- Data mining --- 301 --- AA / International- internationaal --- 681.3*H28 --- 681.3*H2 --- 681.3*H3 --- 681.3*I26 --- 681.3*J3 --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek --- Information storage and retrieval --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 681.3*J3 Life and medical sciences (Computer applications) --- Life and medical sciences (Computer applications) --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*H2 Database management: security; integrity; protection--See also {?681.5*E5} --- Database management: security; integrity; protection--See also {?681.5*E5} --- 681.3*H28 Database applications --- Database applications

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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Statistiekwetenschap --- Wiskundige statistiek --- Statistische fysica --- Moleculaire biologie --- Biologie --- Ingenieurswetenschappen. Technologie --- Programmering --- Informatiesystemen --- Artificiële intelligentie. Robotica. Simulatie. Graphics --- Computer. Informatica. Automatisering --- statistische kwaliteitscontrole --- industriële statistieken --- biologie --- informatica --- database management --- robots --- moleculaire biologie --- statistisch onderzoek --- Bioinformatics. --- Computational intelligence. --- Data mining. --- Forecasting. --- Inference. --- Machine learning. --- Statistics --- Supervised learning (Machine learning). --- Computerintelligentie. --- Statistiek --- Methodology. --- Methodologie. --- MACHINE LEARNING -- 516 --- STATISTICAL LEARNING -- 516 --- SUPERVISED LEARNING -- 516 --- Bioinformatics --- Data mining --- Forecasting --- Inference --- Machine learning --- 519.23 --- 519.2 --- 681.3*I26 --- Learning, Machine --- Artificial intelligence --- Machine theory --- Ampliative induction --- Induction, Ampliative --- Inference (Logic) --- Reasoning --- Forecasts --- Futurology --- Prediction --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Intelligence, Computational --- Soft computing --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- 519.23 Statistical analysis. Inference methods --- Statistical analysis. Inference methods --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Methodology --- Data processing --- Machine Learning --- Computational intelligence --- Statistical methods --- Supervised learning (Machine learning) --- Apprentissage supervisé (Intelligence artificielle) --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B --- Mathematical statistics --- Artificial intelligence. Robotics. Simulation. Graphics --- Statistique mathématique --- Artificial intelligence. --- Probabilities. --- Statistics . --- Bioinformatics . --- Computational biology . --- Artificial Intelligence. --- Data Mining and Knowledge Discovery. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Computational Biology/Bioinformatics. --- Computer Appl. in Life Sciences. --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematical statistics. --- Statistique mathématique --- Statistical decision. --- Statistics - Methodology