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Emotion Recognition in Sound
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Higher School of Economics (HSE) and supported by the Information Retrieval Specialist Group at the British Computer Society (BCS–IRSG). The conference was held during March 24–27, 2013, in Moscow, Russia – the easternmost location in the history of the ECIR series. ECIR 2013 received a total of 287 submissions in three categories: 191 full papers, 78 posters, and 18 demonstrations. The geographical distribution of the submissions is as follows: 70% were from Europe (including 9% from Russia), 17% from Asia, 12% from North and South America, and 3% from the rest of the world. All submissions were reviewed by at least three members of an international two-tier Program Committee. Of the papers submitted to the main research track, 30 were selected for oral presentation and 25 for poster/short presentation (16% and 13%, respectively, hence a 29% acceptance rate). In addition, 38 posters (49%) and 10 demonstrations (56%) were accepted. The accepted contributions represent the state of the art in information retrieval, cover a diverse range of topics, propose novel applications, and indicate promising directions for future research. Out of accepted contributions, 66% have a student as the primary author. We gratefully thank all Program Committee members for their time and efforts ensuring a high-quality level of the ECIR 2013 program. Additionally, ECIR 2013 hosted four tutorials and two workshops covering various IR-related topics. We express our gratitude to the Workshop Chair, Evgeniy Gabrilovich, and the Tutorial Chair, Djoerd Hiemstra, and the members of their committees.
Tutorials:
– Searching the Web of Data
– Practical Online Retrieval Evaluation
– Cross-Lingual Probabilistic Topic Modeling and Its Applications in Information
Retrieval
– Distributed Information Retrieval and Applications
Workshops:
– From Republicans to Teenagers: Group Membership and Search (GRUMPS)
– Integrating IR Technologies for Professional Search
The conference included a Mentoring Program and Doctoral Consortium.
We thank Mikhail Ageev and Hideo Joho and Dmitriy Ignatov, respectively, for coordinating these activities.
VI Preface
We would like to thank our invited speakers – Mor Naaman (Rutgers University, Social Media Information Lab) and the winner of the Karen Sparck Jones award. The Industry Day took place on the final day of the conference and featured a bright assortment of talks given by prominent researchers and practitioners: Paul Ogilvie (LinkedIn), Hilary Mason (bitly), Antonio Gulli (Bing), Andrey Kalinin (Mail.Ru), Jimmy Lin (Twitter/University of Maryland), Marc Najork (Microsoft Research), and Andrey Styskin (Yandex), to whom we express our gratitude. We appreciate generous financial support from Yandex and HSE, as well as from our sponsorsMail.Ru and Russian Foundation for Basic Research (platinum level), Google and ABBYY
В статье дается краткое введение в ансамбли классификаторов в машинном обучении и описывается алгоритм, повышающий качество классификации за счет рекомендации классификаторов объектам. Гипотеза, заложенная в основу алгоритма, состоит в том, что классификатор скорее правильно классифицирует объект, если он правильно предсказал метки соседей этого объекта из обучающей выборки. Автор иллюстрирует принцип алгоритма на простом примере и описывает тестирование на реальных данных.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
The definition of a phoneme as a fuzzy set of minimal speech units from the model database is proposed. On the basis of this definition and the Kullback-Leibler minimum information discrimination principle the novel phoneme recognition algorithm has been developed as an enhancement of the phonetic decoding method. The experimental results in the problems of isolated vowels recognition and word recognition in Russian are presented. It is shown that the proposed method is characterized by the increase of recognition accuracy and reliability in comparison with the phonetic decoding method
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
В работе рассмотрены основные алгоритмы и их программная реализация на платформе Silverlight 4, которые могут применяться для переноса в режим дистанционного обучения систем обучения языку программ "Профессор Хиггинс" компании "ИстраСофт".