Book_ESL_2019: Elements of Statistical Learning |
Website | https://sites.google.com/view/book-esl-2019 |
Submission link | https://easychair.org/conferences/?conf=aml-2019 |
Abstract registration deadline | June 30, 2019 |
Submission deadline | July 31, 2019 |
Statistical learning theory is a framework for machine learning and deep learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, healthcare, business, marketing, bioinformatics and baseball.
The goals of learning are understanding and prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. Supervised learning involves learning from a training set of data. Every point in the training is an input-output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict output from future input. Depending on the type of output, supervised learning problems are either problems of regression or problems of classification. If the output takes a continuous range of values, it is a regression problem while classification problems are those for which the output will be an element from a discrete set of labels. Classification is very common for machine learning applications. In facial recognition, for instance, a picture of a person's face would be the input, and the output label would be that person's name. The input would be represented by a large multidimensional vector whose elements represent pixels in the picture. After learning a function based on the training set data, that function is validated on a test set of data, data that did not appear in the training set.
Aims and Scope
The main scope of this book is to bring together applications of machine learning in artificial intelligence (human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and social sciences, bioinformatics, robotics, etc.) in order to give a wide landscape of techniques that can be successfully applied and also to show how such techniques should be adapted to each particular domain. Topics of interests include, (but are not limited to)
- Classification
- Regression and prediction
- Clustering
- Kernel methods
- Soft computing
- Fuzzy system
- Problem solving and planning
- Reasoning and inference
- Data mining
- Web mining
- Information retrieval
- Natural language processing
- Design and diagnosis
- Deep learning
- Probabilistic Models and Methods
- Vision and speech perception
- Robotics and control
- Multi-agent systems
- Game playing
- Bioinformatics
- Social sciences
- Streaming data
- Music Modelling and Analysis
- Industrial, healthcare, financial and scientific applications of all kind.
Submission Guidelines
1) All submissions will be received through EasyChair on the following link:
https://easychair.org/conferences/?conf=esl-2019
2) Authors must book a slot of "Submission ID" by submitting abstract on or before the deadline of abstract submission.
3) Authors must submit the abstract file in PDF to the easychair.
4) Each of the submitted abstract will go through editorial inquiry for pre-screening of submission based on theme and sub-topics proposed in the book.
5) Authors of selected abstract will be intimated to submit the full chapter for consideration under review.
6) Each submission will go through double blind peer-review process.
7) The authors may be asked to submit the revised version of the manuscripts by replacing the pre-existing file in EasyChair portal.
8) Based on review reports and editorial inquiry, decision of acceptance/rejection will be taken by the editors.
9) All papers must be original and not simultaneously submitted to another journal or conference.
10) The similarity index of submissions must not be greater than 10%.
11) The submissions must be in Communications in Computer and Information Science (CCIS) format only.
https://resource-cms.springernature.com/springer-cms/rest/v1/content/51958/data/v1
12) Editors reserve rights to turn down any manuscript without providing any reason.
13) Decision of the editors shall be binding and no communication will considered regarding the decision of editors over a submission.
14) After final decision author need to submit CRC's original source file of MS Word and Latex along with images as a zipped folder.
15) The manuscript length must not be more than 15 pages according to CCIS format.
Editors
- Prof. Prashant Johri, Galgotias University, Greater Noida, India
- Dr. Jitendra Kumar Verma, Amity University, Gurugram (Manesar), India
- Dr. Sudip Paul, North-Eastern Hill University, Shillong, India
Editorial Advisory Board
- Prof. D. N.Goswami, School of Studies, Jiwaji University, Gwalior, India
- Prof. Syed Hamid Hasan, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah, Saudi Arabia
- Prof. Sunil K.Khatri Pro Vice Chancellor, Amity University, Tashkent, Uzbekistan
- Prof. Masood Mohammadian, Associate Professor, School of Information Technology and Systems, University of Canberra, Canberra, Australia
- Prof. Sunil Vedra, Dean, School of Computing, University of Salford, Manchester, United Kingdom
Deadlines to Remeber
Abstract Submission | 30 June 2019 |
Full Chapter Submission | 31 Aug 2019 |
Review Notification | 30 Sept 2019 |
Final Decision | 31 Oct 2019 |
*CRC Submission of Full Chapter | 30 Nov 2019 |
*CRC is Camera Ready Copy
Publication
Accepted submissions will be published as a chapter of book titled "Elements of Statistical Learning" in Springer Book Series “Algorithms for Intelligent Systems (AIS)”.
Contact
In each communication with editors, authors need to specifily submission number along with the book name for which submission has been done in the subject field of Email. All questions about submissions should be emailed to the following emails:
- Dr. Prashant Johri, johri.prashant@gmail.com
- Dr. Jitendra Kumar Verma , jitendra.verma.in@ieee.org
- Dr. Sudip Paul, sudip.paul.bhu@gmail.com