Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

The European Position Paper on Rhinosinusitis and Nasal Polyps 2020 is the update of similar evidence based position papers published in 2005 and 2007 and 2012. The core objective of the EPOS2020 guideline is to provide revised, up-to-date and clear evidence-based recommendations and integrated care pathways in ARS and CRS. EPOS2020 provides an update on the literature published and studies undertaken in the eight years since the EPOS2012 position paper was published and addresses areas not extensively covered in EPOS2012 such as paediatric CRS and sinus surgery. EPOS2020 also involves new stakeholders, including pharmacists and patients, and addresses new target users who have become more involved in the management and treatment of rhinosinusitis since the publication of the last EPOS document, including pharmacists, nurses, specialised care givers and indeed patients themselves, who employ increasing self-management of their condition using over the counter treatments. The document provides suggestions for future research in this area and offers updated guidance for definitions and outcome measurements in research in different settings. EPOS2020 contains chapters on definitions and classification where we have defined a large number of terms and indicated preferred terms. A new classification of CRS into primary and secondary CRS and further division into localized and diffuse disease, based on anatomic distribution is proposed. There are extensive chapters on epidemiology and predisposing factors, inflammatory mechanisms, (differential) diagnosis of facial pain, allergic rhinitis, genetics, cystic fibrosis, aspirin exacerbated respiratory disease, immunodeficiencies, allergic fungal rhinosinusitis and the relationship between upper and lower airways. The chapters on paediatric acute and chronic rhinosinusitis are totally rewritten. All available evidence for the management of acute rhinosinusitis and chronic rhinosinusitis with or without nasal polyps in adults and children is systematically reviewed and integrated care pathways based on the evidence are proposed. Despite considerable increases in the amount of quality publications in recent years, a large number of practical clinical questions remain. It was agreed that the best way to address these was to conduct a Delphi exercise . The results have been integrated into the respective sections. Last but not least, advice for patients and pharmacists and a new list of research needs are included. The full document can be downloaded for free on the website of this journal: http://www.rhinologyjournal.com.

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European Position Paper on Rhinosinusitis and Nasal Polyps 2020

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

A Comprehensive Survey on Transfer Learning

Independent Component Analysis.

PURPOSE:To shorten the processing time and save the storage area by generating standard patterns by concatenating voice recognition units, regarding words and sentences for a model which outputs an input voice with the highest probability among the standard pattern as a recognition result and voice recognition units as voice events, and representing the standard patterns with a network of the voice events. CONSTITUTION:A vector sequence quantized by a division processing part 3 is divided into voice event sections of prevoicing, etc. A voice event standard pattern generation processing part 4 finds the appearance frequencies of feature vectors included in the voice event sections and normalize the frequencies so that the sum becomes 1, thereby obtaining the output probability in the state of a Markov model corresponding to the voice events. A generation processing part 6 for a word standard model uses a word dictionary 7 and a rewriting rule part 8 to generate a word standard model for words corresponding to the network of the voice events. Here, the probability value of the output of a voice section is calculated from the standard word model as to a voice to be recognized and the words corresponding to the standard model are outputted.

Speech recognition system

A data collection system using a speech recognition system under multiple environments

We propose a framework for arranging audio objects in recorded music using artificial intelligence (AI) to anticipate the preferences of individual listeners. The signals of audio objects, such as the tracks of guitars and drums in a piece of music, are re-synthesizes in order to provide the preferred spatial arrangements of each listener. Deep learning-based noise suppression ratio estimation is utilized as a technique for enhancing audio objects from mixed signals. Neural networks are tuned for each audio object in advance, and noise suppression ratios are estimated for each frequency band and time frame. After enhancing each audio object, the objects are re-synthesized as stereo sound using the positions of the audio objects and the listener as synthesizing parameters. Each listener supplies simple feedback regarding his/her preferred audio object arrangement using graphical user interface (GUI). Using this listener feedback, the synthesizing parameters are then stochastically optimized in accordance w...

AI framework to arrange audio objects according to listener preferences

PURPOSE: To improve an environment adaptive method for improving the recognition rate in a speech recognition using statistical models. CONSTITUTION: As to a voice model 6A and a non-speech model 13B among statistical models, the voice model 6A is made previously from speech data for learning 4 in the same manner as a coneventional manner and the non-speech model 13B is made from data 12 being later a noise superposition by superposing environmental noise data for learning 10 to speed data for learning 4 before the starting of a speech recognition. Then, the non-speech model 13B and the voice model 6A made from the speech data for learning 4 are used in the speech recognition.

Kazuya Takeda

Papers

Speech recognition system

A data collection system using a speech recognition system under multiple environments

AI framework to arrange audio objects according to listener preferences

Environment adaptive method and environment adaptive speech recognition device in speech recognition

CENSREC-3: An Evaluation Framework for Japanese Speech Recognition in Real Car-Driving Environments(Speech and Hearing)