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The standard scientific methodology in linguistics is empirical testing of falsifiable hypotheses. As such the process of hypothesis generation is central, and involves formulation of a research question about a domain of interest and statement of a hypothesis relative to it. In corpus linguistics the domain is text, and generation involves abstraction of data from text, data analysis, and formulation of a hypothesis based on inference from the results. Traditionally this process has been paper-based, but the advent of electronic text has increasingly rendered it obsolete both because the size of digital corpora is now at or beyond the limit of what can efficiently be used in the traditional way, and because the complexity of data abstracted from them can be impenetrable to understanding. Linguists are increasingly turning to mathematical and statistical computational methods for help, and cluster analysis is such a method. It is used across the sciences for hypothesis generation by identification of structure in data which are too large or complex, or both, to be interpretable by direct inspection. This book aims to show how cluster analysis can be used for hypothesis generation in corpus linguistics, thereby contributing to a quantitative empirical methodology for the discipline.

Cluster analysis -- Data processing. --- Corpora (Linguistics) -- Data processing. --- Natural language processing (Computer science). --- Quantitative linguistics. --- Corpora (Linguistics) --- Cluster analysis --- Natural language processing (Computer science) --- Quantitative linguistics --- Computational linguistics --- Languages & Literatures --- Philology & Linguistics --- Data processing --- Cluster-Analyse. --- Korpus (Linguistik) --- Corpus linguistics; cluster analysis; quantitative linguistics; hypothesis generation --- (VLB-WN)1561: Hardcover, Softcover / Allgemeine und Vergleichende Sprachwissenschaft --- Korpus (Linguistik). --- Corpus linguistics; cluster analysis; quantitative linguistics; hypothesis generation. --- (VLB-WN)1561: Hardcover, Softcover / Allgemeine und Vergleichende Sprachwissenschaft. --- Computational linguistics. --- Data processing. --- Automatic language processing --- Language and languages --- Language data processing --- Linguistics --- Natural language processing (Linguistics) --- Applied linguistics --- Cross-language information retrieval --- Mathematical linguistics --- Multilingual computing --- NLP (Computer science) --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Corpus-based analysis (Linguistics) --- Corpus linguistics --- Linguistic analysis (Linguistics) --- Corpus linguistics. --- cluster analysis. --- hypothesis generation. --- quantitative linguistics.

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This monograph presents design methodologies for (robust) fractional control systems. It shows the reader how to take advantage of the superior flexibility of fractional control systems compared with integer-order systems in achieving more challenging control requirements. There is a high degree of current interest in fractional systems and fractional control arising from both academia and industry and readers from both milieux are catered to in the text. Different design approaches having in common a trade-off between robustness and performance of the control system are considered explicitly. The text generalizes methodologies, techniques and theoretical results that have been successfully applied in classical (integer) control to the fractional case. The first part of Advances in Robust Fractional Control is the more industrially-oriented. It focuses on the design of fractional controllers for integer processes. In particular, it considers fractional-order proportional-integral-derivative controllers, because integer-order PID regulators are, undoubtedly, the controllers most frequently adopted in industry. The second part of the book deals with a more general approach to fractional control systems, extending techniques (such as H-infinity optimal control and optimal input‒output inversion based control) originally devised for classical integer-order control. Advances in Robust Fractional Control will be a useful reference for the large number of academic researchers in fractional control, for their industrial counterparts and for graduate students who want to learn more about this subject.

Engineering. --- Control. --- Industrial Chemistry/Chemical Engineering. --- Industrial and Production Engineering. --- Chemical engineering. --- Industrial engineering. --- Ingénierie --- Génie chimique --- Génie industriel --- Cluster analysis. --- Mechanical Engineering --- Engineering & Applied Sciences --- Mechanical Engineering - General --- Robust control --- Fractional calculus. --- Matrhematical models. --- Derivatives and integrals, Fractional --- Differentiation of arbitrary order, Integration and --- Differintegration, Generalized --- Fractional derivatives and integrals --- Generalized calculus --- Generalized differintegration --- Integrals, Fractional derivatives and --- Integration and differentiation of arbitrary order --- Control engineering. --- Production engineering. --- Calculus --- Control and Systems Theory. --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Chemistry, Industrial --- Engineering, Chemical --- Industrial chemistry --- Chemistry, Technical --- Metallurgy --- Manufacturing engineering --- Process engineering --- Industrial engineering --- Mechanical engineering --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers

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This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.

Computer Science. --- Artificial Intelligence (incl. Robotics). --- Data Mining and Knowledge Discovery. --- Computer science. --- Data mining. --- Artificial intelligence. --- Informatique --- Exploration de données (Informatique) --- Intelligence artificielle --- Causation. --- Cluster analysis. --- Mechanical Engineering --- Engineering & Applied Sciences --- Computer Science --- Mechanical Engineering - General --- Causality --- Cause and effect --- Effect and cause --- Final cause --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Beginning --- God --- Metaphysics --- Philosophy --- Necessity (Philosophy) --- Teleology --- Artificial Intelligence. --- 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 --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers