Журналы ВАК по тематике ресурса

JMLR seeks previously unpublished papers on machine learning that contain:

• new algorithms with empirical, theoretical, psychological, or biological justification;
• experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems;
• accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods;
• formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks;
• development of new analytical frameworks that advance theoretical studies of practical learning methods;
• computational models of data from natural learning systems at the behavioral or neural level; or
• extremely well-written surveys of existing work.

Papers published in this journal are geared heavily towards applications, with an anticipated split of 70% of the papers published being applications-oriented, research and the remaining 30% containing more theoretical research.

Topics of interest include but not limited to:

• Biological data pre-processing and cleaning
• Biological data visualisation
• Biological data integration and management
• Phylogenetics
• Biomedical ontologies construction/management
• Microarray data analysis
• Protein/RNA structure prediction
• Genomics and proteomics
• Drug design
• Biomedical literature data mining
• Modelling of biomolecular pathways
• Whole, multiple genome comparison
• Systems biology and pathways
• Biological data curation

Automatic, intelligent processing of documents is at the intersections of many fields of research, especially of computer vision, image analysis, pattern recognition and artificial intelligence, as well as studies on reading, handwriting and linguistics. Although quality document related publications continue to appear in journals dedicated to these domains, the community will benefit from having this journal as a focal point for archival literature dedicated to document analysis and recognition.

This journal publishes articles of four primary types - original research papers, correspondence, overviews and summaries, and research notes. Special issues on active areas of research are encouraged. We welcome submissions in all areas related to document analysis and recognition.

Possible topics include:

- Document Image Processing - Document Models - Handwriting Models and Analysis - Character and Word Recognition - On-line Recognition - Pen Based Computing - Multi-lingual Processing - Physical and Logical Analysis - Graphics Recognition - Map and Line Drawing Understanding - Storage and Retrieval of Documents - Text Analysis and Processing - Natural Language Issues - Information Extraction and Filtering - Performance Evaluation - Document Authentification and Validation - Implementations, Applications and Systems

as well as non-traditional topics such as:

- Processing Text in Other Contexts - Multimedia and Hypermedia Analysis - Time Varying Documents - Distributed Document Collections (Digital Libraries).

• Contains current material on applications and theory research in:

o Image Processing o Natural Language Processing o Computer Vision o Speech Understanding o Pattern Recognition o Robotics and Related Fields o Expert Systems o Artificial Intelligence o Knowledge Engineering o Neural Networks

• Has special issues devoted to relevant and key areas in the field

• Is a worthwhile investment for researchers/libraries concerned with research in industry or academia

We consider pattern recognition in the broad sense, and we assume that the journal will be read by people with a common interest in pattern recognition but from many diverse backgrounds. These include biometrics, target recognition, biological taxonomy, meteorology, space science, classification methods, character recognition, image processing, industrial applications, neural computing, and many others.

The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition.

Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• statistical, structural, syntactic pattern recognition; • neural networks, machine learning, data mining; • discrete geometry, algebraic, graph-based techniques for pattern recognition; • signal analysis, image coding and processing, shape and texture analysis; • computer vision, robotics, remote sensing; • document processing, text and graphics recognition, digital libraries; • speech recognition, music analysis, multimedia systems; • natural language analysis, information retrieval; • biometrics, biomedical pattern analysis and information systems; • scientific, engineering, social and economical applications of pattern recognition; • special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.

Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.

The journal encourages the submission of original case-studies on applied pattern recognition which describe important research in the area. The journal also solicits reviews on novel pattern analysis benchmarks, evaluation of pattern analysis tools, and important research activities at international centres of excellence working in pattern analysis.

• automated reasoning • computational theories of learning • heuristic search • knowledge representation • qualitative physics • signal, image and speech understanding • robotics • natural language understanding • software and hardware architectures for AI.

The journal reports results achieved; proposals for new ways of looking at AI problems must include demonstrations of effectiveness. From time to time, the journal publishes survey articles.

Particular attention is given to:

• AI-based clinical decision making • medical knowledge engineering • knowledge-based and agent-based systems • computational intelligence in bio- and clinical medicine • intelligent medical information systems • AI in medical education • intelligent devices and instruments • automated reasoning and metareasoning in medicine • methodological, philosophical, ethical, and social issues of AI in medicine

AIIM features:

• original research contributions • methodological reviews • survey papers • special issue articles • position papers • historical perspectives • editorials • guest editorials • letters to the editor • book reviews

The Review publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms from these fields.

Artificial Intelligence Review also presents refereed survey and tutorial articles, as well as reviews and commentary on significant developments from these disciplines.

FOCAL TOPICS OF COMPUTATIONAL INTELLIGENCE

Machine learning - including in particular symbolic multistrategy and cognitive learning. Multistrategy learning systems integrate two or more inference types and/or representational mechanisms. These systems take advantage of the strengths of individual learning strategies, and therefore can be applied to a wider range of problems. Human learning is clearly not limited to any single strategy, but can involve any type of strategy, or a combination of them, depending on the task at hand. Research on multistrategy learning is therefore key to understanding learning processes in general, to making progress in machine learning, as well as to extending the applicability of current machine learning methods to new practical domains.

Web intelligence and semantic web. Web intelligence is concerned with the application of AI to the next generation of web systems, services and resources. These include better search/retrieval algorithms, client side systems (e.g. more effective agents) and server side systems (e.g. effective ways to present material on web pages and throughout web sites, including adaptive websites and personalized interfaces).

The semantic web is an extension to the World Wide Web, in which web content is expressed in a form that is accessible to programs (software agents), following the vision of the web as universal medium for data, information and knowledge exchange.

Discovery science and knowledge mining. Discovery science (also known as discovery-based science) is a scientific methodology which emphasizes analysis of large volumes of experimental data or text data with the goal of finding new patterns or correlations, leading to hypothesis formation and other scientific methodologies. Tools of interest include: Data Mining: looking for associations or relationships in operational or transactional data; Text Mining and Information Extraction: looking for concepts and their associations or relationships in natural language text; Structured, semi-structured and unstructured text mining; Text Summarization: extracting terms and phrases from large text document collections that summarize their content; Web mining: Web structure, content and usage mining; Ontology Learning from Text and Data bases.

Agents and multiagent systems. Agents as a computational abstraction have replaced "objects," and have provided the necessary ingredients to move to societies of interacting intelligent entities, based on things like the belief-desire-intention (BDI) model of agent societies, market economies, e-commerce models and game theory. Such abstractions are dispersed throughout the scientific world, depending largely on applications. Multiagent systems (MAS) are systems in which many intelligent autonomous agents interact with each other. Agents can be either cooperative, pursuing a common goal, or selfish, going after their own interests. Architectures, interaction protocols and languages must be developed for multiagent systems. Topics of interest include: Autonomy-oriented computing; Agent systems methodology and language; Agent-based simulation and modeling; Agent-based applications; Agent-based negotiation and autonomous auction; Distributed problem solving.

Modern knowledge-based systems. Knowledge-based systems aim to make expertise available for decision making, and information sharing, when and where needed. The next generation of such systems needs to tap into large knowledge bases of domain-specific knowledge, which combine machine learning and structured background knowledge representation, such as ontology, and causal representations and reasoning. Information sharing is concerned with creating collaborative knowledge environments for sharing and disseminating information.

Key application areas of AI. Entertainment and Gaming, Software Engineering, Business, Finance, Commerce and Economics, Knowledge-based and Personalized User Interfaces.

We aim to make the journal the focus of key application areas, where AI is making a significant impact, but lack a coherent publication venue. These include: Entertainment and Game Development, i.e. building game engines using AI techniques; Software Engineering, including program understanding, software repositories and reverse engineering; Business, Finance, Commerce and Economics: learning aggregate behaviours (e.g. stock market trends) or modeling individual and group demographics (e.g. web mining); Knowledge-based and Personalized User Interfaces, to make interaction clearer to the user and more efficient, with better support for the users' goals, and efficient presentation of complex information.

Artificial Intelligence (AI) techniques are now being used by the practising engineer to solve a whole range of hitherto intractable problems. This journal provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering.Focal points ... click here for full Aims & Scope

Artificial Intelligence (AI) techniques are now being used by the practising engineer to solve a whole range of hitherto intractable problems. This journal provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering.

Focal points of the journal are:

• Applications of real-time intelligent automation, and their associated supporting methodologies and techniques. • Architectures, algorithms and techniques for distributed AI systems. • Decision-support systems. • Aspects of reasoning: abductive, case-based, model-based, non-monotonic, incomplete, progressive and approximate reasoning. • Other theoretical aspects, e.g. chaos theory and fractals. • Knowledge processing, e.g. a priori and self-learning, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems, neural networks, fuzzy systems and genetic algorithms. • Perception, e.g. image and signal processing, pattern recognition, vision systems, tactile systems, and speech recognition and synthesis. • Aspects of software engineering, e.g. intelligent programming environments, the testing verification and validation of AI-based software, software and hardware architectures for the real-time use of AI techniques, safety and reliability. • Fault detection, fault analysis and diagnostics. • Industrial experiences in the application of the above techniques, e.g. case studies or bench-marking exercises.

Topics covered by IJAIT include but are not limited to: AI in Bioinformatics, AI for Service Engineering, AI for Software Engineering, AI for Ubiquitous Computing, AI for Web Intelligence Applications, AI Parallel Processing Tools (hardware/software), AI Programming Languages, AI Tools for CAD and VLSI Analysis/Design/Testing, AI Tools for Computer Vision and Speech Understanding, AI Tools for Multimedia, Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Image Understanding, Integrated/Hybrid AI Approaches, Intelligent System Architectures, Knowledge-Based/Expert Systems, Knowledge Management and Processing Tools, Knowledge Representation Languages, Natural Language Understanding, Neural Networks for AI, Object-Oriented Programming for AI, Reasoning and Evolution of Knowledge Bases, Self-Healing and Autonomous Systems, and Software Engineering for AI.

IJAIT publishes high-quality, original research papers as well as state-of-the-art surveys related to AI tools on a bi-monthly basis in two types, full and short papers. It also publishes book reviews and brief updates on AI tools.

Special issues related to the topics of the journal are welcome. A short proposal should be submitted to the Editor-in-Chief. It should include: a tentative title; name(s) and address(es) of the Guest Editor(s); purpose and scope; possible contributors; and a timetable (deadlines for "call for papers" and review processes, intended publication date, etc.) The whole process should take about a year. If the proposal is accepted, the Guest Editor(s) will be responsible for the special issue and should follow the normal IJAIT review process, which requires five reviewers for each received paper. Copies of the reviewed papers and the reviewers' comments should be given to the Editor-in-Chief for recording purposes.

The journal also publishes comprehensive review articles.

Among the disciplines represented are statistics, psychology, biology, information retrieval, anthropology, archeology, astronomy, business, chemistry, computer science, economics, engineering, geography, geology, linguistics, marketing, mathematics, medicine, political science, psychiatry, sociology, and soil science.

Published twice a year, each issue typically comprises four sections: articles, short notes and comments, software abstracts, and book reviews.

Mathematical Statistics

Mathematics provides the language in which models and the properties of statistical methods are formulated. It is essential for rigor, coherence, clarity and understanding. Consequently, our policy is to continue to play a special role in presenting research at the forefront of mathematical statistics, especially theoretical advances that are likely to have a significant impact on statistical methodology or understanding.

Applied and Interdisciplinary Statistics

Substantive fields are essential for continued vitality of statistics since they provide the motivation and direction for most of the future developments in statistics. We thus intend to publish papers relating to the role of statistics in interdisciplinary investigations in all fields of natural, medical, technical and social science.

Computational Statistics

A third force that continues to reshape statistics is the computational revolution, and the Annals will welcome developments in this area. Both in applied and in computational statistics, submissions will be evaluated primarily by the relevance of the issues addressed and the creativity of the proposed solutions.

1. Applications 2. Algorithms 3. Bayesian Methods 4. Causal Inference & Missing Data 5. Computer Intensive Inference 6. Decision Theory 7. Errors in Variables 8. Experimental Design 9. Functional Data Analysis 10. Machine Learning/High Dimensional Data Analysis 11. Graphical Modeling 12. Hypothesis Testing 13. Nonparametric Estimation/Inference 14. Parametric Inference 15. Robust Statistics 16. Semiparametric Inference 17. Sequential Methods 18. Spatial Statistics 19. Survival Analysis 20. Survey Sampling/ Survey Research 21. Stochastic Processes 22. Applied Probability

AISM features high quality papers across the broad spectrum of statistics. In particular, the journal emphasizes papers that (a) establish new application areas, (b) present new procedures and algorithms, (c) develop unifying theories, (d) analyze and improve existing procedures and theories, and (e) communicate empirical findings supported by real data.

In addition to papers by professional statisticians, the journal welcomes contributions from authors in related fields.

Computational Statistics& Data Analysis, the official journal of the International Association of Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of three refereed sections, divided ... click here for full Aims & Scope

Computational Statistics& Data Analysis, the official journal of the International Association of Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of three refereed sections, divided into thefollowing subject areas:

I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., bioinformatics, computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, neural networks, numerical methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms.

II) Statistical Methodology for Data Analysis - Manuscripts dealing with data analysis strategies and methodologies (e.g., biostatistics, classification, clinical trial methodology, data exploration, density estimation, design of experiments, model free data exploration, pattern recognition/image analysis, robust procedures, statistical genetics).

III) Special Applications - Manuscripts at the interface of statistics and computers (e.g., comparison of statistical methodology, computer-assisted instruction for statistics, simulation experiments).

• Nonparametric modeling
• Nonparametric function estimation
• Rank and other robust and distribution-free procedures
• Resampling methods
• Lack-of-fit testing
• Multivariate analysis
• Inference with high-dimensional data
• Dimension reduction and variable selection
• Methods for errors in variables, missing, censored, and other incomplete data structures
• Inference of stochastic processes
• Sample surveys
• Time series analysis
• Longitudinal and functional data analysis
• Nonparametric Bayes methods and decision procedures
• Semiparametric models and procedures
• Statistical methods for imaging and tomography
• Statistical inverse problems
• Financial statistics and econometrics
• Bioinformatics and comparative genomics
• Statistical algorithms and machine learning.

Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order.

Fields covered include computer algorithms related to probability or statistics, studies in statistical inference by means of simulation techniques, and implementation of interactive statistical systems.

It is expected that the papers give interesting and novel contributions to statistical theory and its applications at a good mathematical level. The results should be presented in form of theorems together with their mathematical proofs, which should not be merely routine calculations. Additionally, the discussion of results and their value for the theory or for applications could be a valuable addition, as well as numerical results on the efficiency or examples for the application of the theoretical results.

A special section is devoted to survey papers on theory and methods in interesting areas of statistics. The Journal may also publish proceedings of conferences and announcements on related topics.

STATISTICS & PROBABILITY LETTERS is a refereed journal. Articles will be limited to six journal pages (13 double-space typed pages) including references and figures. Apart from the six-page limitation, originality, quality and clarity will be the criteria for choosing the material to be published in STATISTICS & PROBABILITY LETTERS. Every attempt will be made to provide the first review of a submitted manuscript within three months of submission.

The proliferation of literature and long publication delays have made it difficult for researchers and practitioners to keep up with new developments outside of, or even within, their specialization. The aim of STATISTICS & PROBABILITY LETTERS is to help to alleviate this problem. Concise communications (letters) allow readers to quickly and easily digest large amounts of material and to stay up-to-date with developments in all areas of statistics and probability.

The mainstream of LETTERS will focus on new statistical methods, theoretical results, and innovative applications of statistics and probability to other scientific disciplines, especially biostatistics and bioinformatics. Key results and central ideas must be presented in a clear and concise manner. These results may be part of a larger study that the author will submit at a later time as a full length paper to SPL or to another journal. Theory and methodology may be published with proofs omitted, or only sketched, but only if sufficient support material is provided so that the findings can be verified. Empirical and computational results that are of significant value will be published. We also plan to publish applications and case studies that demonstrate a novel use of existing techniques or have interesting innovative ideas about data collection, modelling or inference.

A partial list of topics includes techniques for evaluating analytically intractable problems, search and optimization methods, computer-intensive (resampling) methods, simulation and Monte Carlo, graphics, computer environments, reliability of hardware, statistical approaches to software errors, information retrieval, and statistics of databases.

In addition to original research reports, the journal publishes authoritative topical reviews, discussions about papers, and book and software reviews. Periodically, special issues are published that are dedicated to important and emerging topics or the proceedings of relevant conferences.