54 research outputs found
Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data
Functional and longitudinal data are becoming more and more common in practice. This paper focuses on sparse and irregular longitudinal data with a multicategory response. The predictor consists of sparse and irregular observations, potentially contaminated with measurement errors, on the predictor trajectory. To deal with this type of complicated predictors, we borrow the strength of large margin classifiers in statistical learning for classification of sparse and irregular longitudinal data. In particular, we propose functional robust truncated-hinge-loss support vector machines to perform multicategory classification with the aid of functional principal component analysis
Classification-based method for estimating dynamic treatment regimes
Dynamic treatment regimes are sequential decision rules dictating how to individualize treatments to patients based on evolving treatments and covariate history. In this thesis, we investigate two methods of estimating dynamic treatment regimes. The first method extends outcome weighted learning from two-treatments to multi-treatments and allows for negative treatment outcome. We show that under two different sets of assumptions, the Fisher consistency can be maintained. The second method estimates treatment rules by a neural classification tree. A weighted squared loss function is defined to approximate the indicator function to maintain the smoothness. A method of tree reconstruction and pruning is proposed to increase the interpretability. Simulation studies and real application to data from Sequential Treatment Alternatives to Relieve Depression (STAR*D) clinical trial are conducted to illustrate the proposed methods
Bayesian classification and survival analysis with curve predictors
We propose classification models for binary and multicategory data where the
predictor is a random function. The functional predictor could be irregularly and
sparsely sampled or characterized by high dimension and sharp localized changes. In
the former case, we employ Bayesian modeling utilizing flexible spline basis which is
widely used for functional regression. In the latter case, we use Bayesian modeling
with wavelet basis functions which have nice approximation properties over a large
class of functional spaces and can accommodate varieties of functional forms observed
in real life applications. We develop an unified hierarchical model which accommodates
both the adaptive spline or wavelet based function estimation model as well as
the logistic classification model. These two models are coupled together to borrow
strengths from each other in this unified hierarchical framework. The use of Gibbs
sampling with conjugate priors for posterior inference makes the method computationally
feasible. We compare the performance of the proposed models with the naive
models as well as existing alternatives by analyzing simulated as well as real data. We
also propose a Bayesian unified hierarchical model based on a proportional hazards model and generalized linear model for survival analysis with irregular longitudinal
covariates. This relatively simple joint model has two advantages. One is that using
spline basis simplifies the parameterizations while a flexible non-linear pattern of
the function is captured. The other is that joint modeling framework allows sharing
of the information between the regression of functional predictors and proportional
hazards modeling of survival data to improve the efficiency of estimation. The novel
method can be used not only for one functional predictor case, but also for multiple
functional predictors case. Our methods are applied to analyze real data sets and
compared with a parameterized regression method
ODN: Opening the Deep Network for Open-set Action Recognition
In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories. However, action recognition in the real world is essentially
an \textit{open-set} problem, namely, it is impossible to know all action
categories beforehand and consequently infeasible to prepare sufficient
training samples for those emerging categories. In this case, applying
closed-set recognition methods will definitely lead to unseen-category errors.
To address this challenge, we propose the Open Deep Network (ODN) for the
open-set action recognition task. Technologically, ODN detects new categories
by applying a multi-class triplet thresholding method, and then dynamically
reconstructs the classification layer and "opens" the deep network by adding
predictors for new categories continually. In order to transfer the learned
knowledge to the new category, two novel methods, Emphasis Initialization and
Allometry Training, are adopted to initialize and incrementally train the new
predictor so that only few samples are needed to fine-tune the model. Extensive
experiments show that ODN can effectively detect and recognize new categories
with little human intervention, thus applicable to the open-set action
recognition tasks in the real world. Moreover, ODN can even achieve comparable
performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201
Investigations in adaptive processing of multispectral data
Adaptive data processing procedures are applied to the problem of classifying objects in a scene scanned by multispectral sensor. These procedures show a performance improvement over standard nonadaptive techniques. Some sources of error in classification are identified and those correctable by adaptive processing are discussed. Experiments in adaptation of signature means by decision-directed methods are described. Some of these methods assume correlation between the trajectories of different signature means; for others this assumption is not made
Second order cone programming approaches for handling missing and uncertain data
We propose a novel second order cone programming formulation for designing robust classifiers
which can handle uncertainty in observations. Similar formulations are also derived for designing
regression functions which are robust to uncertainties in the regression setting. The proposed formulations
are independent of the underlying distribution, requiring only the existence of second order
moments. These formulations are then specialized to the case of missing values in observations
for both classification and regression problems. Experiments show that the proposed formulations
outperform imputation
Ellipsoidal classification via semidefinite programming
We propose a classification approach exploiting relationships between ellipsoidal separation and Support-vector Machine (SVM) with quadratic kernel. By adding a (Semidefinite Programming) SDP constraint to SVM model we ensure that the chosen hyperplane in feature space represents a non-degenerate ellipsoid in input space. This allows us to exploit SDP techniques within Support-vector Regression (SVR) approaches, yielding better results in case ellipsoid-shaped separators are appropriate for classification tasks. We compare our approach with spherical separation and SVM on some classification problems
Modelos de aprendizaje automático en la detección e identificación de personas: una revisión de literatura
Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020.
Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review?
Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people.
Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria.
Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes.
Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes.
Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people.
Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.Introducción: Este artículo es el resultado de la investigación titulada " Desarrollo de un prototipo para optimizar las condiciones de acceso al SENA-Pescadero utilizando inteligencia artificial y herramientas de código abierto", desarrollada en el Servicio Nacional de Aprendizaje en 2020.
Problema: ¿Cómo identificar las técnicas de aprendizaje automático aplicadas a los procesos de visión por computador a través de una revisión bibliográfica?
Objetivo: Determinar la aplicación, así como las ventajas y desventajas de las técnicas de aprendizaje automático enfocadas a la detección e identificación de personas.
Metodología: Revisión sistemática de la literatura en 4 bases de datos bibliográficas y científicas de alto impacto, utilizando filtros de búsqueda y criterios de selección de información.
Resultados: Técnicas de aprendizaje automático definidas como Análisis de Componentes Principales, Codificación Local de Coordenadas Regularizada de Etiquetas Débiles, Máquinas de Vectores de Soporte, Clasificadores en Cascada de Haar y EigenFaces y FisherFaces, así como su aplicabilidad en procesos de detección e identificación.
Conclusiones: La investigación permitió identificar las principales técnicas de inteligencia computacional basadas en machine learning aplicadas a la detección e identificación de personas. Su influencia se mostró en varios casos de aplicación, pero la mayoría de ellos se centraron en la implementación y optimización de sistemas de control de acceso, o tareas en las que se requería la identificación de personas para la ejecución de procesos
Originalidad: A través de esta investigación se estudiaron y definieron las principales técnicas de machine learning utilizadas actualmente para la detección e identificación de personas
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Supervised gamma process Poisson factorization
textThis thesis develops the supervised gamma process Poisson factorization (S-GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels. S-GPPF is fully generative and nonparametric: document labels and count matrices are modeled under a unified probabilistic framework and the number of latent topics is controlled automatically via a gamma process prior. The framework provides for multi-class classification of documents using a generative max-margin classifier. Several recent data augmentation techniques are leveraged to provide for exact inference using a Gibbs sampling scheme. The first portion of this thesis reviews supervised topic modeling and several key mathematical devices used in the formulation of S-GPPF. The thesis then introduces the S-GPPF generative model and derives the conditional posterior distributions of the latent variables for posterior inference via Gibbs sampling. The S-GPPF is shown to exhibit state-of-the-art performance for joint topic modeling and document classification on a dataset of conference abstracts, beating out competing supervised topic models. The unique properties of S-GPPF along with its competitive performance make it a novel contribution to supervised topic modeling.Electrical and Computer Engineerin
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