47,004 research outputs found
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Stochastic Training of Neural Networks via Successive Convex Approximations
This paper proposes a new family of algorithms for training neural networks
(NNs). These are based on recent developments in the field of non-convex
optimization, going under the general name of successive convex approximation
(SCA) techniques. The basic idea is to iteratively replace the original
(non-convex, highly dimensional) learning problem with a sequence of (strongly
convex) approximations, which are both accurate and simple to optimize.
Differently from similar ideas (e.g., quasi-Newton algorithms), the
approximations can be constructed using only first-order information of the
neural network function, in a stochastic fashion, while exploiting the overall
structure of the learning problem for a faster convergence. We discuss several
use cases, based on different choices for the loss function (e.g., squared loss
and cross-entropy loss), and for the regularization of the NN's weights. We
experiment on several medium-sized benchmark problems, and on a large-scale
dataset involving simulated physical data. The results show how the algorithm
outperforms state-of-the-art techniques, providing faster convergence to a
better minimum. Additionally, we show how the algorithm can be easily
parallelized over multiple computational units without hindering its
performance. In particular, each computational unit can optimize a tailored
surrogate function defined on a randomly assigned subset of the input
variables, whose dimension can be selected depending entirely on the available
computational power.Comment: Preprint submitted to IEEE Transactions on Neural Networks and
Learning System
Scalable Data Augmentation for Deep Learning
Scalable Data Augmentation (SDA) provides a framework for training deep
learning models using auxiliary hidden layers. Scalable MCMC is available for
network training and inference. SDA provides a number of computational
advantages over traditional algorithms, such as avoiding backtracking, local
modes and can perform optimization with stochastic gradient descent (SGD) in
TensorFlow. Standard deep neural networks with logit, ReLU and SVM activation
functions are straightforward to implement. To illustrate our architectures and
methodology, we use P\'{o}lya-Gamma logit data augmentation for a number of
standard datasets. Finally, we conclude with directions for future research
Incremental Sparse Bayesian Ordinal Regression
Ordinal Regression (OR) aims to model the ordering information between
different data categories, which is a crucial topic in multi-label learning. An
important class of approaches to OR models the problem as a linear combination
of basis functions that map features to a high dimensional non-linear space.
However, most of the basis function-based algorithms are time consuming. We
propose an incremental sparse Bayesian approach to OR tasks and introduce an
algorithm to sequentially learn the relevant basis functions in the ordinal
scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression
(ISBOR), automatically optimizes the hyper-parameters via the type-II maximum
likelihood method. By exploiting fast marginal likelihood optimization, ISBOR
can avoid big matrix inverses, which is the main bottleneck in applying basis
function-based algorithms to OR tasks on large-scale datasets. We show that
ISBOR can make accurate predictions with parsimonious basis functions while
offering automatic estimates of the prediction uncertainty. Extensive
experiments on synthetic and real word datasets demonstrate the efficiency and
effectiveness of ISBOR compared to other basis function-based OR approaches
Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
- …