2,094 research outputs found
Boosting Functional Response Models for Location, Scale and Shape with an Application to Bacterial Competition
We extend Generalized Additive Models for Location, Scale, and Shape (GAMLSS)
to regression with functional response. This allows us to simultaneously model
point-wise mean curves, variances and other distributional parameters of the
response in dependence of various scalar and functional covariate effects. In
addition, the scope of distributions is extended beyond exponential families.
The model is fitted via gradient boosting, which offers inherent model
selection and is shown to be suitable for both complex model structures and
highly auto-correlated response curves. This enables us to analyze bacterial
growth in \textit{Escherichia coli} in a complex interaction scenario,
fruitfully extending usual growth models.Comment: bootstrap confidence interval type uncertainty bounds added; minor
changes in formulation
On-Line Learning and Wavelet-Based Feature Extraction Methodology for Process Monitoring using High-Dimensional Functional Data
The recent advances in information technology, such as the various automatic data acquisition systems and sensor systems, have created tremendous opportunities for collecting valuable process data. The timely processing of such data for meaningful information remains a challenge. In this research, several data mining methodology that will aid information streaming of high-dimensional functional data are developed.
For on-line implementations, two weighting functions for updating support vector regression parameters were developed. The functions use parameters that can be easily set a priori with the slightest knowledge of the data involved and have provision for lower and upper bounds for the parameters. The functions are applicable to time series predictions, on-line predictions, and batch predictions. In order to apply these functions for on-line predictions, a new on-line support vector regression algorithm that uses adaptive weighting parameters was presented. The new algorithm uses varying rather than fixed regularization constant and accuracy parameter. The developed algorithm is more robust to the volume of data available for on-line training as well as to the relative position of the available data in the training sequence. The algorithm improves prediction accuracy by reducing uncertainty in using fixed values for the regression parameters. It also improves prediction accuracy by reducing uncertainty in using regression values based on some experts’ knowledge rather than on the characteristics of the incoming training data. The developed functions and algorithm were applied to feedwater flow rate data and two benchmark time series data. The results show that using adaptive regression parameters performs better than using fixed regression parameters.
In order to reduce the dimension of data with several hundreds or thousands of predictors and enhance prediction accuracy, a wavelet-based feature extraction procedure called step-down thresholding procedure for identifying and extracting significant features for a single curve was developed. The procedure involves transforming the original spectral into wavelet coefficients. It is based on multiple hypothesis testing approach and it controls family-wise error rate in order to guide against selecting insignificant features without any concern about the amount of noise that may be present in the data. Therefore, the procedure is applicable for data-reduction and/or data-denoising. The procedure was compared to six other data-reduction and data-denoising methods in the literature. The developed procedure is found to consistently perform better than most of the popular methods and performs at the same level with the other methods.
Many real-world data with high-dimensional explanatory variables also sometimes have multiple response variables; therefore, the selection of the fewest explanatory variables that show high sensitivity to predicting the response variable(s) and low sensitivity to the noise in the data is important for better performance and reduced computational burden. In order to select the fewest explanatory variables that can predict each of the response variables better, a two-stage wavelet-based feature extraction procedure is proposed. The first stage uses step-down procedure to extract significant features for each of the curves. Then, representative features are selected out of the extracted features for all curves using voting selection strategy. Other selection strategies such as union and intersection were also described and implemented. The essence of the first stage is to reduce the dimension of the data without any consideration for whether or not they can predict the response variables accurately. The second stage uses Bayesian decision theory approach to select some of the extracted wavelet coefficients that can predict each of the response variables accurately. The two stage procedure was implemented using near-infrared spectroscopy data and shaft misalignment data. The results show that the second stage further reduces the dimension and the prediction results are encouraging
Dynamically optimal treatment allocation using Reinforcement Learning
Devising guidance on how to assign individuals to treatment is an important
goal in empirical research. In practice, individuals often arrive sequentially,
and the planner faces various constraints such as limited budget/capacity, or
borrowing constraints, or the need to place people in a queue. For instance, a
governmental body may receive a budget outlay at the beginning of a year, and
it may need to decide how best to allocate resources within the year to
individuals who arrive sequentially. In this and other examples involving
inter-temporal trade-offs, previous work on devising optimal policy rules in a
static context is either not applicable, or sub-optimal. Here we show how one
can use offline observational data to estimate an optimal policy rule that
maximizes expected welfare in this dynamic context. We allow the class of
policy rules to be restricted for legal, ethical or incentive compatibility
reasons. The problem is equivalent to one of optimal control under a
constrained policy class, and we exploit recent developments in Reinforcement
Learning (RL) to propose an algorithm to solve this. The algorithm is easily
implementable with speedups achieved through multiple RL agents learning in
parallel processes. We also characterize the statistical regret from using our
estimated policy rule by casting the evolution of the value function under each
policy in a Partial Differential Equation (PDE) form and using the theory of
viscosity solutions to PDEs. We find that the policy regret decays at a
rate in most examples; this is the same rate as in the static case.Comment: 67 page
Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics
Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data.
The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications
GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY
The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user\u27s interest is an important task. Existing works on patter
Deep learning and feature engineering techniques applied to the myoelectric signal for accurate prediction of movements
Técnicas de reconhecimento de padrões no Sinal Mioelétrico (EMG) são empregadas no
desenvolvimento de próteses robóticas, e para isso, adotam diversas abordagens de Inteligência Artificial (IA). Esta Tese se propõe a resolver o problema de reconhecimento de padrões
EMG através da adoção de técnicas de aprendizado profundo de forma otimizada. Para isso,
desenvolveu uma abordagem que realiza a extração da caracterÃstica a priori, para alimentar
os classificadores que supostamente não necessitam dessa etapa. O estudo integrou a plataforma BioPatRec (estudo e desenvolvimento avançado de próteses) a dois algoritmos de
classificação (Convolutional Neural Network e Long Short-Term Memory) de forma hÃbrida,
onde a entrada fornecida à rede já possui caracterÃsticas que descrevem o movimento (nÃvel
de ativação muscular, magnitude, amplitude, potência e outros). Assim, o sinal é rastreado
como uma série temporal ao invés de uma imagem, o que nos permite eliminar um conjunto
de pontos irrelevantes para o classificador, tornando a informação expressivas. Na sequência,
a metodologia desenvolveu um software que implementa o conceito introduzido utilizando
uma Unidade de Processamento Gráfico (GPU) de modo paralelo, esse incremento permitiu
que o modelo de classificação aliasse alta precisão com um tempo de treinamento inferior a 1
segundo. O modelo paralelizado foi chamado de BioPatRec-Py e empregou algumas técnicas
de Engenharia de Features que conseguiram tornar a entrada da rede mais homogênea, reduzindo a variabilidade, o ruÃdo e uniformizando a distribuição. A pesquisa obteve resultados
satisfatórios e superou os demais algoritmos de classificação na maioria dos experimentos
avaliados. O trabalho também realizou uma análise estatÃstica dos resultados e fez o ajuste
fino dos hiper-parâmetros de cada uma das redes. Em última instancia, o BioPatRec-Py forneceu um modelo genérico. A rede foi treinada globalmente entre os indivÃduos, permitindo
a criação de uma abordagem global, com uma precisão média de 97,83%.Pattern recognition techniques in the Myoelectric Signal (EMG) are employed in the
development of robotic prostheses, and for that, they adopt several approaches of Artificial
Intelligence (AI). This Thesis proposes to solve the problem of recognition of EMG standards
through the adoption of profound learning techniques in an optimized way. The research
developed an approach that extracts the characteristic a priori to feed the classifiers that
supposedly do not need this step. The study integrated the BioPatRec platform (advanced
prosthesis study and development) to two classification algorithms (Convolutional Neural
Network and Long Short-Term Memory) in a hybrid way, where the input provided to the
network already has characteristics that describe the movement (level of muscle activation,
magnitude, amplitude, power, and others). Thus, the signal is tracked as a time series instead
of an image, which allows us to eliminate a set of points irrelevant to the classifier, making the
information expressive. In the sequence, the methodology developed software that implements
the concept introduced using a Graphical Processing Unit (GPU) in parallel this increment
allowed the classification model to combine high precision with a training time of less than
1 second. The parallel model was called BioPatRec-Py and employed some Engineering
techniques of Features that managed to make the network entry more homogeneous, reducing
variability, noise, and standardizing distribution. The research obtained satisfactory results
and surpassed the other classification algorithms in most of the evaluated experiments. The
work performed a statistical analysis of the outcomes and fine-tuned the hyperparameters of
each of the networks. Ultimately, BioPatRec-Py provided a generic model. The network was
trained globally between individuals, allowing the creation of a standardized approach, with
an average accuracy of 97.83%
Novel Image Representations and Learning Tasks
abstract: Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201
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