Prediction of Ion Drift Times for a Proteome-Wide Peptide Set Using Partial Least Squares Regression, Least-Squares Support Vector Machine and Gaussian Process

2009 ◽  
Vol 28 (11-12) ◽  
pp. 1386-1393 ◽  
Author(s):  
Xiuhong Liu ◽  
Jun Liang ◽  
Jicai Fan ◽  
Zhicai Shang
Keyword(s):  
Support Vector Machine ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Support Vector ◽  
Least Squares Regression ◽  
Ion Drift

Anwendung spektroskopischer Messverfahren und Multivariater Datenanalyse zur Bewertung und Beobachtung von Bioprozessen

2017 ◽  
Author(s):  
Jan-Patrick Voß
Keyword(s):  
Pichia Pastoris ◽  
Least Squares ◽  
Support Vector Regression ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Process Analytical Technology ◽  
Support Vector ◽  
Least Squares Regression ◽  
Analytical Technology

Die vorliegende Arbeit befasst sich mit dem Bioprozessmonitoring unter Verwendung spektroskopischer Messverfahren und multivariater Datenanalyse nach den Grundsätzen von PAT – Process Analytical Technology. Mit NIR-Spektroskopie und dem Verfahren Soft Independent Modelling of Class Analogy (SIMCA) wurde eine Quali¬tätsbewertung von Hefeextrakten realisiert. Im Vordergrund stand jedoch die Quanti¬fizierung nicht direkt messbarer Größen aus NIR-, Raman- und 2D-Fluoreszenzspektren in pharmazeutischen Produktionsprozessen mit Pichia pastoris. Eine entsprechende Online-Bestimmung mit der Methode Partial Least Squares Regression (PLSR) kam weiterführend zur Regelung der Glycerolkonzentration zum Einsatz. Darüber hinaus wurde die Verwendung nichtspektraler Online-Daten zur Prozessbeobachtung erprobt. Dabei gelang mit Hilfe des nichtlinearen Verfahrens Support Vector Regression (SVR) unter anderem die Bestimmung zellspezifischer Reaktionsraten. ...

scholarly journals Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015 ◽  
pp. 55 ◽  
Author(s):  
Ll. Pérez-Planells ◽  
J. Delegido ◽  
J. P. Rivera-Caicedo ◽  
J. Verrelst
Keyword(s):  
Linear Regression ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Partial Least Squares Regression ◽  
Gaussian Process Regression ◽  
Least Squares Regression ◽  
Kernel Ridge Regression

<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>

scholarly journals Mid- to long-term runoff prediction by combining the deep belief network and partial least-squares regression

2020 ◽  
Vol 22 (5) ◽  
pp. 1283-1305
Author(s):  
Zhaoxin Yue ◽  
Ping Ai ◽  
Chuansheng Xiong ◽  
Min Hong ◽  
Yanhong Song
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Deep Belief Network ◽  
Support Vector ◽  
Complex Nature ◽  
Least Squares Regression ◽  
Belief Network ◽  
Runoff Prediction

Abstract Data representation and prediction model design play an important role in mid- to long-term runoff prediction. However, it is challenging to extract key factors that accurately characterize the changes in the runoff of a river basin because of the complex nature of the runoff process. In addition, the low accuracy is another problem for mid- to long-term runoff prediction. With an aim to solve these problems, two improvements are proposed in this paper. First, the partial mutual information (PMI)-based approach was employed for estimating the importance of various factors. Second, a deep learning architecture was introduced by using the deep belief network (DBN) with partial least-squares regression (PLSR), together denoted as PDBN, for mid- to long-term runoff prediction, which solves the problem of parameter optimization for the DBN using PLSR. The novelty of the proposed method lies in the key factor selection and a novel forecasting method for mid- to long-term runoff. Experimental results demonstrated that the proposed method can significantly improve the effect of mid- to long-term runoff prediction. Also, compared with the results obtained by current state-of-the-art prediction methods, i.e., DBN, backpropagation neural networks, and support vector machine models, our prediction results demonstrate the performance of the proposed method.

scholarly journals A novel pedestrian detection algorithm based on data fusion of face images

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984527
Author(s):  
Jianhu Zheng ◽  
Jinshuan Peng
Keyword(s):  
Face Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Experimental Result ◽  
Support Vector ◽  
Least Squares Regression ◽  
Face Images

In order to facilitate effective crime prevention and to issue timely warnings for the sake of public security, it is important to pinpoint the accurate position of particular pedestrians in crowded areas. Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. The primary challenge for researchers is to obtain a stable result while the targeted face is subject to varying conditions—particularly of illumination. To address this issue, we propose a novel pedestrian detection algorithm with multisource face images, which involves a face recognition algorithm based on the conjugate orthonormalized partial least-squares regression analysis under a complex lighting environment. Statistical learning theory is a research specialization of machine learning, especially applicable to small samples. Building upon the theoretical principles used to solve small-sample statistical problems, a new hypothesis has been developed; using this concept, we integrate the conjugate orthonormalized partial least-squares regression with the revised support vector machine algorithm to undertake the solution of the facial recognition problem. The experimental result proves that our algorithm achieves better performance when compared with other state-of-the-art methodologies, both numerically and visually.

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