We accept files in .docx/.doc/.pdf/Latex Pdf format - as per the call for papers schedule.
Papers should be thoroughly checked and proofread before submission. After you have submitted your article you are unable to make any changes to it during the refereeing process—although if accepted, you will have a chance to make minor revisions after refereeing and before the final submission of your paper.
Paper content must be original and relevant to one of the many conference topics.
Authors are required to ensure accuracy of quotations, citations, diagrams, maps, and tables.
Figures and tables need to be placed where they are to appear in the text and must be clear and easy to view.
Papers must follow format according to the template which will be sent to accepted papers.
Machine learningSupervised learning Ranking Supervised learning by classification Supervised learning by regression Structured outputs Cost-sensitive learning Unsupervised learning Cluster analysis Anomaly detection Mixture modeling Topic modeling Source separation Motif discovery Dimensionality reduction and manifold learning Reinforcement learning Sequential decision making Inverse reinforcement learning Apprenticeship learning Multi-agent reinforcement learning Adversarial learning Multi-task learning Transfer learning Lifelong machine learning Learning under covariate shift Learning settings Batch learning Online learning settings Learning from demonstrations Learning from critiques Learning from implicit feedback Active learning settings Semi-supervised learning settings Machine learning approaches Classification and regression trees Kernel methods Support vector machines Gaussian processes Neural networks Logical and relational learning Inductive logic learning Statistical relational learning Learning in probabilistic graphical models Maximum likelihood modeling Maximum entropy modeling Maximum a posteriori modeling Mixture models Latent variable models Bayesian network models Learning linear models Perceptron algorithm Factorization methods Non-negative matrix factorization Factor analysis Principal component analysis Canonical correlation analysis Latent Dirichlet allocation Rule learning Instance-based learning Markov decision processes Partially-observable Markov decision processes Stochastic games Learning latent representations Deep belief networks Bio-inspired approaches Artificial life Evolvable hardware Genetic algorithms Genetic programming Evolutionary robotics Generative and developmental approaches Machine learning algorithms Dynamic programming for Markov decision processes Value iteration Q-learning Policy iteration Temporal difference learning Approximate dynamic programming methods Ensemble methods Boosting Bagging Spectral methods Feature selection Regularization Cross-validation