scholarly journals Multiple-View Active Learning for Environmental Sound Classification

2016 ◽  
Vol 12 (12) ◽  
pp. 49 ◽  
Author(s):  
Yan Zhang ◽  
Danjv Lv ◽  
Yili Zhao
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Environmental Sound ◽  
Single View ◽  
Feature Sets ◽  
Multiple View ◽  
Sound Classification ◽  
Training Examples ◽  
The Given ◽  
Sound Data

<p class="Abstract">Multi-view learning with multiple distinct feature sets is a rapid growing direction in machine learning with boosting the performance of supervised learning classification under the case of few labeled data. The paper proposes Multi-view Simple Disagreement Sampling (MV-SDS) and Multi-view Entropy Priority Sampling (MV-EPS) methods as the selecting samples strategies in active learning with multiple-view. For the given environmental sound data, the CELP features in 10 dimensions and the MFCC features in 13 dimensions are two views respectively. The experiments with a single view single classifier, SVML, MV-SDS and MV-EPS on the environmental sound extracted two of views, CELP &amp; MFCC are carried out to illustrate the results of the proposed methods and their performances are compared under different percent training examples. The experimental results show that multi-view active learning can effectively improve the performance of classification for environmental sound data, and MV-EPS method outperforms the MV-SDS.</p><div> </div>

scholarly journals MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification

2021 ◽  
Vol 11 (18) ◽  
pp. 8394
Author(s):  
Lancelot Lhoest ◽  
Mimoun Lamrini ◽  
Jurgen Vandendriessche ◽  
Nick Wouters ◽  
Bruno da Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sound Recognition ◽  
Learning Models ◽  
Environmental Sound ◽  
Machine Learning Classifiers ◽  
Sound Classification ◽  
Learning Classifiers ◽  
Constrained Devices ◽  
Environmental Sound Recognition

Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on constrained devices. The experimental results show that classical machine learning classifiers can be combined to achieve similar results to deep learning models, and even outperform them in accuracy. The cost, however, is a larger classification time.

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals THE APPLICATION OF MACHINE LEARNING TOOLS TO THE VALIDATION OF AN AIR TRAFFIC CONTROL DOMAIN THEORY

2001 ◽  
Vol 10 (04) ◽  
pp. 613-637 ◽  
Author(s):  
M. M. WEST ◽  
T. L. McCLUSKEY
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
Domain Theory ◽  
Learning Tools ◽  
General Clause ◽  
Faulty Component ◽  
Training Examples ◽  
Mixed Initiative

In this paper we describe a project (IMPRESS) in which machine learning (ML) tools were created and utilised for the validation of an Air Traffic Control domain theory written in first order logic. During the project, novel techniques were devised for the automated revision of general clause form theories using training examples. These techniques were combined in an algorithm which focused in on the parts of a theory which involve ordinal sorts, and applied geometrical revision operators to repair faulty component parts. While we illustrate the feasibility of applying ML to this area, we conclude that to be effective it must be focused to the application at hand, and used in mixed-initiative mode within a tools environment. The method is illustrated with experimental results obtained during the project.

Borderline and Depression: A Thin EEG Line

2021 ◽  
pp. 155005942110608
Author(s):  
Jakša Vukojević ◽  
Damir Mulc ◽  
Ivana Kinder ◽  
Eda Jovičić ◽  
Krešimir Friganović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
Everyday Clinical Practice ◽  
Data Set ◽  
Learning Techniques ◽  
Eeg Recordings ◽  
The Given ◽  
Close Interrelationship

In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal ◽  
Alvaro Vidal Torreira
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Design Optimization ◽  
Fuel Injection ◽  
A Priori ◽  
Computational Cost ◽  
Learning Approach ◽  
Data Generation ◽  
Compression Ignition Engine ◽  
Hyperparameter Selection

AbstractThe use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

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