Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The theory of formal languages is the oldest and most fundamental area of theoretical computer science. It has served as a basis of formal modeling from the early stages of programming languages to the recent beginnings of DNA computing. This first handbook of formal languages gives a comprehensive up-to-date coverage of all important aspects and subareas of the field. Best specialists of various subareas, altogether 50 in number, are among the authors. The maturity of the field makes it possible to include a historical perspective in many presentations. The individual chapters can be studied independently, both as a text and as a source of reference. The Handbook is an invaluable aid for advanced students and specialists in theoretical computer science and related areas in mathematics, linguistics, and biology.

Handbook of Formal Languages

Publisher Summary
This chapter presents the field of finite automata, which is a branch of mathematics connected with the algebraic theory of semigroups and associative algebras. From another viewpoint, finite automata are a branch of algorithm design concerned with string manipulation and sequence processing. The chapter presents the basic definitions and a proof of the fundamental result of S. C. Kleene. The chapter discusses the notion of star-height, which is one of the fast developing areas of automata theory. The chapter discusses star-free sets, which is an important notion especially because of its connection with logic. The chapter presents a complete proof of P. Schutzenberger's theorem, stating the equality between star-free sets and aperiodic sets. It also presents the syntactic characterization of two important subfamilies of star-free sets, namely, locally testable and piecewise testable. The chapter describes the applications of finite automata, such as string matching or file indexing. The chapter introduces the field of automata recognizing numbers expanded at some basis. This is an aspect of finite automata regarding several fields in classical mathematics, such as number theory and ergodic theory.

CHAPTER 1 – Finite Automata

Parallelism and concurrency are fundamental concepts in computer science. Specification and verification of concurrent programs are of first importance. It concerns our daily life whether software written for distributed systems behaves correctly.

Partial commutation and traces

The aim of this paper is the study of asynchronous automata, a special kind of automata which encode the independency relation between actions and which enable their concurrent execution. These automata, introduced by Zielonka (RAIRO Inform. Theor. Appl.21, 99-135 (1987)), constitute a natural extension of finite automata to the case of asynchronous parallelism. Their behaviour is described by trace languages, subsets of partially commutative monoids. The main result concerning this class of automata states that they accept exactly all recognizable trace languages. In this paper we give new improved constructions of asynchronous automata. In the final part of the paper we present a distributed system of messages with bounded time-stamps based on asynchronous automata.

Asynchronous Mappings and Asynchronous Cellular Automata

https://hal.inria.fr/inria-00288445/document

Streaming tree automata

We study the problem of update translation for views on XML documents More precisely, given an XML view definition and a user defined view update program, find a source update program that translates the view update without side effects on the view Additionally, we require the translation to be defined on all possible source documents; this corresponds to Hegner's notion of uniform translation The existence of such translation would allow to update XML views without the need of materializationThe class of views we consider can remove parts of the document and rename nodes Our update programs define the simultaneous application of a collection of atomic update operations among insertion/deletion of a subtree and node renaming Such update programs are compatible with the XQuery Update Facility (XQUF) snapshot semantics Both views and update programs are represented by recognizable tree languages We present as a proof of concept a small fragment of XQUF that can be expressed by our update programs, thus allows for update propagationTwo settings for the update problem are studied: without source constraints, where all source updates are allowed, and with source constraints, where there is a restricted set of authorized source updates Using tree automata techniques, we establish that without constraints, all view updates are uniformly translatable and the translation is tractable In presence of constraints, not all view updates are uniformly translatable However, we introduce a reasonable restriction on update programs for which uniform translation with constraints becomes possible

/pdf/view-update-translation-for-xml-2nr881lqlt.pdf

View update translation for XML

In this paper, we revisit the view based security framework for XML without imposing any of the previously considered restrictions on the class of queries, the class of DTDs, and the type of annotations used to define the view. First, we show that the full class of Regular XPath queries is closed under query rewriting . Next, we address the problem of constructing a DTD that describes the view schema, which in general needs not be regular. We propose three different methods of approximating the view schema and we show that the produced DTDs are indistinguishable from the exact schema (with queries from a class specific for each method). Finally, we investigate problems of static analysis of security access specifications.

/pdf/xml-security-views-revisited-2o1l39f070.pdf

XML Security Views Revisited

We consider several questions about monotone AC-tree automata, a class of equational tree automata whose transition rules correspond to rules in Kuroda normal form of context-sensitive grammars. Whereas it has been proved that this class has a decision procedure to determine if, given a monotone AC-tree automaton, it accepts no terms, other important decidability or complexity results have not been well-investigated yet. In the paper, we prove that the membership problem for monotone AC-tree automata is PSPACE-complete. We then study the expressiveness of monotone AC-tree automata: precisely, we prove that the family of AC-regular tree languages is strictly subsumed in that of AC-monotone tree languages. The proof technique used in obtaining the above result yields the answers to two different questions, specifically that the family of monotone AC-tree languages is not closed under complementation, and that the inclusion problem for monotone AC-tree automata is undecidable.

/pdf/monotone-ac-tree-automata-3efq1s4bcu.pdf

Monotone AC-Tree automata

In this paper, we define the notion of biRFSA which is a residual finate state automaton (RFSA) whose the reverse is also an RFSA. The languages recognized by such automata are called biRFSA languages. We prove that the canonical RFSA of a biRFSA language is a minimal NFA for this language and that each minimal NFA for this language is a sub-automaton of the canonical RFSA. This leads to a characterization of the family of biRFSA languages. In the second part of this paper, we define the family of biseparable automata. We prove that every biseparable NFA is uniquely minimal among all NFAs recognizing a same language, improving the result of H. Tamm and E. Ukkonen for bideterministic automata.

/pdf/minimal-nfa-and-birfsa-languages-480cs27a1z.pdf

Minimal NFA and biRFSA Languages

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