Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I / / edited by Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski

• Record Nr.: UNISA996465761003316
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• ISBN: 3-319-71249-7
• Edizione: [1st ed. 2017.]
• Descrizione fisica: 1 online resource (LXIII, 852 p. 245 illus.)
• Collana: Lecture Notes in Artificial Intelligence ; ; 10534
• Disciplina: 006.31
• Soggetti: Data mining; Artificial intelligence; Optical data processing; Application software; Computer security; Computers; Data Mining and Knowledge Discovery; Artificial Intelligence; Image Processing and Computer Vision; Information Systems Applications (incl. Internet); Systems and Data Security; Computing Milieux
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Anomaly Detection -- Concentration Free Outlier Detection -- Efficient top rank optimization with gradient boosting for supervised anomaly detection -- Robust, Deep and Inductive Anomaly Detection -- Sentiment Informed Cyberbullying Detection in Social Media -- zooRank: Ranking Suspicious Activities in Time-Evolving Tensors -- Computer Vision -- Alternative Semantic Representations for Zero-Shot Human Action Recognition -- Early Active Learning with Pairwise ; Constraint for Person Re-identification -- Guiding InfoGAN with Semi-Supervision -- Scatteract: Automated extraction of data from scatter plots -- Unsupervised Diverse Colorization via Generative Adversarial Networks -- Ensembles and Meta Learning -- Dynamic Ensemble Selection with Probabilistic Classifier Chains -- Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks -- Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks -- Feature Selection and Extraction -- Deep Discrete Hashing with Self-supervised Labels -- Including multi-feature interactions and redundancy for feature ranking in mixed datasets -- Non-redundant Spectral Dimensionality Reduction -- Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-links -- SetExpan: Corpus-based Set Expansion via Context Feature Selection and Rank Ensemble -- Kernel Methods -- Bayesian Nonlinear Support Vector Machines for Big Data -- Entropic Trace Estimation for Log Determinants -- Fair Kernel Learning -- GaKCo: a Fast Gapped k-mer string Kernel using Counting -- Graph Enhanced Memory Networks for Sentiment Analysis -- Kernel Sequential Monte Carlo -- Learning Lukasiewicz Logic Fragments by Quadratic Programming -- Nystrom sketching -- Learning and Optimization -- Crossprop: learning representations by stochastic meta-gradient descent in neural networks -- Distributed Stochastic Optimization of the Regularized Risk via Saddle-point Problem -- Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves using Previous Builds -- Matrix and Tensor Factorization -- Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation -- Content-Based Social Recommendation with Poisson Matrix Factorization -- C-SALT: Mining Class-Speci_c ALTerations in Boolean Matrix Factorization -- Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition -- Structurally Regularized Non-negative Tensor Factorization for Spatio-temporal Pattern Discoveries -- Networks and Graphs -- Attributed Graph Clustering with Unimodal Normalized Cut -- K-clique-graphs for Dense Subgraph Discovery -- Learning and Scaling Directed Networks via Graph Embedding -- Local Lanczos Spectral Approximation for Membership Identification -- Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms -- Survival Factorization for Topical Cascades on Diffusion Networks -- The network-untangling problem: From interactions to activity timelines.-TransT: Type-based Multiple Embedding Representations for Knowledge Graph Completion -- Neural Networks and Deep Learning -- A network Architecture for Multi-multi Instance Learning -- CON-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec -- Deep Over-sampling Framework for Classifying Imbalanced Data -- FCNNs: Fourier Convolutional Neural Networks -- Joint User Modeling across Aligned Heterogeneous Sites using Neural Networks -- Sequence Generation with Target Attention -- Wikipedia Vandal Early Detection: from User Behavior to User Embedding. .
• Sommario/riassunto: The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; ; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

The A-Z of the PhD Trajectory : A Practical Guide for a Successful Journey / / by Eva O. L. Lantsoght

• Record Nr.: UNINA9910299534403321
• Autore: Lantsoght Eva O. L.
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
• ISBN: 9783319774251; 3319774255
• Edizione: [1st ed. 2018.]
• Descrizione fisica: 1 online resource (XIII, 393 p. 59 illus., 6 illus. in color.)
• Collana: Springer Texts in Education, , 2366-7680
• Disciplina: 808.02
• Soggetti: Research - Methodology; Dissertations, Academic; Education, Higher; Maturation (Psychology); Penmanship; Research Skills; Thesis and Dissertation; Higher Education; Personal Development; Writing Skills
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 1. Introduction -- 2. Getting started with a PhD -- 3. Planning your time -- 4. Developing your literature review -- 5. Formulating your research question -- 6. Preparing and executing experiments -- 7. Honing your academic writing skills -- 8. Presenting your work -- 9. ; Communicating science in the 21st century -- 10. Preparing for your first conference -- 11. Writing your first journal article -- 12. Compiling your work into a dissertation -- 13. Navigating career options after your PhD -- 14. Epilogue -- Glossary. .
• Sommario/riassunto: This textbook is a guide to success during the PhD trajectory. The first part of this book takes the reader through all steps of the PhD trajectory, and the second part contains a unique glossary of terms and explanation relevant for PhD candidates. Written in the accessible language of the PhD Talk blogs, the book contains a great deal of practical advice for carrying out research, and presenting one’s work. It includes tips and advice from current and former PhD candidates, thus representing a broad range of opinions. The book includes exercises that help PhD candidates get their work kick-started. It covers all steps of a doctoral journey in STEM: getting started in a program, planning the work, the literature review, the research question, experimental work, writing, presenting, online tools, presenting at one’s first conference, writing the first journal paper, writing and defending the thesis, and the career after the PhD. Since a PhD trajectory is a deeply personal journey, this book suggests methods PhD candidates can try out, and teaches them how to figure out for themselves which proposed methods work for them, and how to find their own way of doing things.