133 research outputs found
Semiannual report, 1 October 1990 - 31 March 1991
Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science is summarized
Three-way Imbalanced Learning based on Fuzzy Twin SVM
Three-way decision (3WD) is a powerful tool for granular computing to deal
with uncertain data, commonly used in information systems, decision-making, and
medical care. Three-way decision gets much research in traditional rough set
models. However, three-way decision is rarely combined with the currently
popular field of machine learning to expand its research. In this paper,
three-way decision is connected with SVM, a standard binary classification
model in machine learning, for solving imbalanced classification problems that
SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy
twin support vector machine with three-way membership (TWFTSVM) are proposed.
The new three-way fuzzy membership function is defined to increase the
certainty of uncertain data in both input space and feature space, which
assigns higher fuzzy membership to minority samples compared with majority
samples. To evaluate the effectiveness of the proposed model, comparative
experiments are designed for forty-seven different datasets with varying
imbalance ratios. In addition, datasets with different imbalance ratios are
derived from the same dataset to further assess the proposed model's
performance. The results show that the proposed model significantly outperforms
other traditional SVM-based methods
Distributed Quantile Regression Analysis and a Group Variable Selection Method
This dissertation develops novel methodologies for distributed quantile regression analysis
for big data by utilizing a distributed optimization algorithm called the alternating direction
method of multipliers (ADMM). Specifically, we first write the penalized quantile regression
into a specific form that can be solved by the ADMM and propose numerical algorithms
for solving the ADMM subproblems. This results in the distributed QR-ADMM
algorithm. Then, to further reduce the computational time, we formulate the penalized
quantile regression into another equivalent ADMM form in which all the subproblems have
exact closed-form solutions and hence avoid iterative numerical methods. This results in the
single-loop QPADM algorithm that further improve on the computational efficiency of the
QR-ADMM. Both QR-ADMM and QPADM enjoy flexible parallelization by enabling data
splitting across both sample space and feature space, which make them especially appealing
for the case when both sample size n and feature dimension p are large.
Besides the QR-ADMM and QPADM algorithms for penalized quantile regression, we
also develop a group variable selection method by approximating the Bayesian information
criterion. Unlike existing penalization methods for feature selection, our proposed gMIC
algorithm is free of parameter tuning and hence enjoys greater computational efficiency.
Although the current version of gMIC focuses on the generalized linear model, it can be
naturally extended to the quantile regression for feature selection.
We provide theoretical analysis for our proposed methods. Specifically, we conduct numerical
convergence analysis for the QR-ADMM and QPADM algorithms, and provide
asymptotical theories and oracle property of feature selection for the gMIC method. All
our methods are evaluated with simulation studies and real data analysis
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion
Research on deep learning-powered voice conversion (VC) in speech-to-speech
scenarios is getting increasingly popular. Although many of the works in the
field of voice conversion share a common global pipeline, there is a
considerable diversity in the underlying structures, methods, and neural
sub-blocks used across research efforts. Thus, obtaining a comprehensive
understanding of the reasons behind the choice of the different methods in the
voice conversion pipeline can be challenging, and the actual hurdles in the
proposed solutions are often unclear. To shed light on these aspects, this
paper presents a scoping review that explores the use of deep learning in
speech analysis, synthesis, and disentangled speech representation learning
within modern voice conversion systems. We screened 621 publications from more
than 38 different venues between the years 2017 and 2023, followed by an
in-depth review of a final database consisting of 123 eligible studies. Based
on the review, we summarise the most frequently used approaches to voice
conversion based on deep learning and highlight common pitfalls within the
community. Lastly, we condense the knowledge gathered, identify main challenges
and provide recommendations for future research directions
Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches
This paper utilizes a novel data on consumer choice under uncertainty, obtained in a laboratory experiment in order to gain substantive knowledge of individual decision-making and to test the best modeling strategy. We compare the performance of logistic regression, discriminant analysis, naïve Bayes classifier, neural network, decision tree, and Random Forest (RF) to discover that the RF model robustly registers the highest classification accuracy. This model also reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects
Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches
This paper utilizes a novel data on consumer choice under uncertainty, obtained in a laboratory experiment in order to gain substantive knowledge of individual decision-making and to test the best modeling strategy. We compare the performance of logistic regression, discriminant analysis, naïve Bayes classifier, neural network, decision tree, and Random Forest (RF) to discover that the RF model robustly registers the highest classification accuracy. This model also reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects
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