DOCSLIB.ORG

Final Project Corrected

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Final Project Corrected Table of Contents...........................................................................1 List of Figures................................................................................................ 3 List of Tables ................................................................................................. 4 C h a p t e r 1 ................................................................................................ 6 I n t r o d u c t i o n........................................................................................................ 6 1.1 Motivation............................................................................................................. 7 1.2 Aim of the study.................................................................................................... 7 1.3 Objectives ............................................................................................................. 8 1.4 Project layout ........................................................................................................ 8 C h a p t e r 2 ................................................................................................ 9 L i t e r a t u r e R e v i e w .......................................................................................... 9 2.1 Definitions............................................................................................................. 9 2.2 Estimating animal abundance ............................................................................. 10 2.1.1 Line transect sampling ................................................................................. 10 2.1.2 Mark-recapture methods .............................................................................. 17 2.1.3 Brief review of State Space Models of population abundance.................... 21 2.3 Resampling ......................................................................................................... 22 2.3.1 Randomisation exact testing ........................................................................ 23 2.3.2 Cross validation ........................................................................................... 24 2.3.3 Jackknife ...................................................................................................... 26 2.3.4 The bootstrap method .................................................................................. 28 C h a p t e r 3 .............................................................................................. 51 M e t h o d o l o g y ...................................................................................................... 51 3.1 Source and nature of the data.............................................................................. 51 3.2 Proposed model................................................................................................... 51 3.2.1 Algorithm 3.1............................................................................................... 54 3.3 Bootstrap application .......................................................................................... 56 3.4 Confidence intervals ........................................................................................... 60 3.4.1 Standard confidence interval: ...................................................................... 60 3.4.2 Percentile confidence intervals: ................................................................... 61 3.5 Monte Carlo simulation ...................................................................................... 61 3.6 Jackknife application .......................................................................................... 62 3. 7 Software and package ........................................................................................ 63 C h a p t e r 4 .............................................................................................. 64 W h i t e R h i n o R e s u l t s ................................................................................... 64 4.1 Calculation of net losses ..................................................................................... 64 4.2 Model fitting algorithm 3.1................................................................................. 64 4.3 Bootstrapping...................................................................................................... 67 4.3.1 200 Replications........................................................................................... 67 4.3.2 1000 Replications......................................................................................... 71 4.4 Comparison of bootstrap estimates and the full sample estimates of the true.... 74 population sizes......................................................................................................... 74 4.5 Monte Carlo simulations..................................................................................... 75 4.5.1 Estimated coverage probability.................................................................... 76 1 4.5.2 Average length of confidence interval......................................................... 77 4.6 Jackknife ............................................................................................................. 79 4.6.1 Results obtained on removing each time point............................................ 80 4.6.2 Jackknife standard error for the sample estimates ....................................... 84 4.7 Comparison of the bootstrap and jackknife ........................................................ 85 4.7.1 Comparison of the bootstrap and jackknife estimates ................................. 85 4.7.2 Comparison of the bootstrap and jackknife standard errors ........................ 86 C h a p t e r 5 .............................................................................................. 88 B l a c k R h i no R e s u l t s..................................................................................... 88 5.1 Calculation of Net Losses ................................................................................... 88 5.2 Model fitting algorithm 3.1................................................................................. 88 5.3 Bootstrapping...................................................................................................... 90 5.3.1 200 Replications........................................................................................... 90 5.3.2 1000 Replications......................................................................................... 93 5.4 Comparison of bootstrap estimates and the full sample model of the true......... 96 population sizes.................................................................................................. 96 5.5 Monte Carlo Simulations .................................................................................... 98 5.5.1 Estimated coverage probability.................................................................... 98 5.5.2 Average length of confidence interval......................................................... 99 5.6 Jackknife ........................................................................................................... 101 5.6.1 Results obtained on removing each time point.......................................... 101 5.6.2 Jackknife standard error for the sample estimates ..................................... 105 5.7 Comparison of the bootstrap and jackknife ...................................................... 106 5.7.1 Comparison of the bootstrap and jackknife estimates ............................... 106 5.7.2 Comparison of the bootstrap and jackknife standard errors ...................... 107 C h a p t e r 6 ............................................................................................ 108 S u m m a r y a n d R e c o m m e n d a t i o n s.................................................. 108 6.1 White Rhino...................................................................................................... 108 6.2 Black Rhino ...................................................................................................... 109 6.3 Discussion......................................................................................................... 109 A p p e n d i c e s........................................................................................ 120 Appendix A: Datasets used..................................................................................... 120 Appendix B: Matlab program for fitting the model................................................ 121 Appendix C: Matlab program for bootstrap application......................................... 122 Appendix D: Matlab program for percentile confidence intervals ......................... 123 Appendix E: Matlab program for Monte Carlo simulation .................................... 124 Appendix E: Matlab program for the jackknife...................................................... 127 References.................................................................................................. 111 2 List of Figures Figure 2.1: Example of line transect sampling and measurement .................................... 13 Figure 4.1: Plot of the estimated true population sizes and the survey estimates at each of the eight surveys ....................................................................................................... 66 Figure 4.2: Plot of the bootstrap
Load more

Recommended publications
  • Notes on Jackknife
    Stat 427/627 (Statistical Machine Learning, Baron) Notes on Jackknife Jackknife is an effective resampling method that was proposed by Morris Quenouille to estimate and reduce the bias of parameter estimates. 1.1 Jackknife resampling Resampling refers to sampling from the original sample S with certain weights. The original weights are (1/n) for each units in the sample, and the original empirical distribution is 1 mass at each observation x , i ∈ S ˆ i F = n 0 elsewhere Resampling schemes assign different weights. Jackknife re-assigns weights as follows, 1 mass at each observation x , i ∈ S, i =6 j ˆ i FJK = n − 1 0 elsewhere, including xj That is, Jackknife removes unit j from the sample, and the new jackknife sample is S(j) =(x1,...,xj−1, xj+1,...,xn). 1.2 Jackknife estimator ˆ ˆ ˆ Suppose we estimate parameter θ with an estimator θ = θ(x1,...,xn). The bias of θ is Bias(θˆ)= Eθ(θˆ) − θ. • How to estimate Bias(θˆ)? • How to reduce |Bias(θˆ)|? • If the bias is not zero, how to find an estimator with a smaller bias? For almost all reasonable and practical estimates, Bias(θˆ) → 0, as n →∞. Then, it is reasonable to assume a power series of the type a a a E (θˆ)= θ + 1 + 2 + 3 + ..., θ n n2 n3 with some coefficients {ak}. 1 1.2.1 Delete one Based on a Jackknife sample S(j), we compute the Jackknife version of the estimator, ˆ ˆ ˆ θ(j) = θ(S(j))= θ(x1,...,xj−1, xj+1,...,xn), whose expected value admits representation a a E (θˆ )= θ + 1 + 2 + ...
    [Show full text]
  • An Unbiased Variance Estimator of a K-Sample U-Statistic with Application to AUC in Binary Classification" (2019)
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Wellesley College Wellesley College Wellesley College Digital Scholarship and Archive Honors Thesis Collection 2019 An Unbiased Variance Estimator of a K-sample U- statistic with Application to AUC in Binary Classification Alexandria Guo [email protected] Follow this and additional works at: https://repository.wellesley.edu/thesiscollection Recommended Citation Guo, Alexandria, "An Unbiased Variance Estimator of a K-sample U-statistic with Application to AUC in Binary Classification" (2019). Honors Thesis Collection. 624. https://repository.wellesley.edu/thesiscollection/624 This Dissertation/Thesis is brought to you for free and open access by Wellesley College Digital Scholarship and Archive. It has been accepted for inclusion in Honors Thesis Collection by an authorized administrator of Wellesley College Digital Scholarship and Archive. For more information, please contact [email protected]. An Unbiased Variance Estimator of a K-sample U-statistic with Application to AUC in Binary Classification Alexandria Guo Under the Advisement of Professor Qing Wang A Thesis Submitted in Partial Fulfillment of the Prerequisite for Honors in the Department of Mathematics, Wellesley College May 2019 © 2019 Alexandria Guo Abstract Many questions in research can be rephrased as binary classification tasks, to find simple yes-or- no answers. For example, does a patient have a tumor? Should this email be classified as spam? For classifiers trained to answer these queries, area under the ROC (receiver operating characteristic) curve (AUC) is a popular metric for assessing the performance of a binary classification method, where a larger AUC score indicates an overall better binary classifier.
    [Show full text]
  • Parametric, Bootstrap, and Jackknife Variance Estimators for the K-Nearest Neighbors Technique with Illustrations Using Forest Inventory and Satellite Image Data
    Remote Sensing of Environment 115 (2011) 3165–3174 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data Ronald E. McRoberts a,⁎, Steen Magnussen b, Erkki O. Tomppo c, Gherardo Chirici d a Northern Research Station, U.S. Forest Service, Saint Paul, Minnesota USA b Pacific Forestry Centre, Canadian Forest Service, Vancouver, British Columbia, Canada c Finnish Forest Research Institute, Vantaa, Finland d University of Molise, Isernia, Italy article info abstract Article history: Nearest neighbors techniques have been shown to be useful for estimating forest attributes, particularly when Received 13 May 2011 used with forest inventory and satellite image data. Published reports of positive results have been truly Received in revised form 6 June 2011 international in scope. However, for these techniques to be more useful, they must be able to contribute to Accepted 7 July 2011 scientific inference which, for sample-based methods, requires estimates of uncertainty in the form of Available online 27 August 2011 variances or standard errors. Several parametric approaches to estimating uncertainty for nearest neighbors techniques have been proposed, but they are complex and computationally intensive. For this study, two Keywords: Model-based inference resampling estimators, the bootstrap and the jackknife, were investigated
    [Show full text]
  • Resolving Finite Source Attributes of Moderate Magnitude Earthquakes
    Project report for SCEC award # 20115: Resolving finite source attributes of moderate magnitude earthquakes of the 2019 Ridgecrest earthquake sequence Wenyuan Fan (PI) & Haoran Meng (Co-PI) 1 Project Objectives and Summary The project aims to investigate rup- ture processes of moderate to small MPM magnitude earthquakes of the 2019 36°N Ridgecrest earthquake sequence and quantify the uncertainties of the ob- A tained source parameters. We are 55' M 4.47 currently working on a manuscript to summarize the project findings. 50' Upon submission, we will upload the data products (source time func- tions and the second moments mod- 45' B’ els) and the processed waveforms, including all the P- and S-waveforms 40' of the target earthquakes and eGfs Ridgecrest that were used for the analysis, to the UCSD Library Digital Collec- 35' B tions (library.ucsd.edu/dc) and Zen- M 5.5 4.5 3.5 odo (zenodo.org). A’ 30' Depth (km) Understanding key kinematic fi- 0510 15 nite source parameters of a large 50' 45' 40' 117°W 30' 25' 20' 15' population of earthquakes can pro- 35.00' vide observational constraints for Figure 1: The 2019 Ridgecrest earthquakes and the regional earthquake physics, fault zone prop- seismic networks (Caltech.Dataset., 2013; Cochran et al., 2020a). The dots are the resolved 39 3.8 ≤ M ≤ 5.5 earthquakes in erties, and the conditions of rupture the region. The triangles are broadband or strong motion sta- dynamics. The related source param- tions. The beach ball shows the focal mechanism of an exam- eter, e.g., earthquake stress-drop, re- ple Mw 4.5 earthquake.
    [Show full text]
  • An Introduction to Bootstrap Methods and Their Application
    An Introduction to Bootstrap Methods and their Application Prof. Dr. Diego Kuonen, CStat PStat CSci Statoo Consulting, Berne, Switzerland @DiegoKuonen + [email protected] + www.statoo.info ‘WBL in Angewandter Statistik ETHZ 2017/19’ — January 22 & 29, 2018 Copyright c 2001–2018 by Statoo Consulting, Switzerland. All rights reserved. No part of this presentation may be reprinted, reproduced, stored in, or introduced into a retrieval system or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, scanning or otherwise), without the prior written permission of Statoo Consulting, Switzerland. Permission is granted to print and photocopy these notes within the ‘WBL in Angewandter Statistik’ at the Swiss Federal Institute of Technology Zurich, Switzerland, for nonprofit educational uses only. Written permission is required for all other uses. Warranty: none. Presentation code: ‘WBL.Statistik.ETHZ.2018’. Typesetting: LATEX, version 2. PDF producer: pdfTEX, version 3.141592-1.40.3-2.2 (Web2C 7.5.6). Compilation date: 12.01.2018. About myself (about.me/DiegoKuonen) PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. MSc in Mathematics, EPFL, Lausanne, Switzerland. • CStat (‘Chartered Statistician’), Royal Statistical Society, UK. • PStat (‘Accredited Professional Statistician’), American Statistical Association, USA. • CSci (‘Chartered Scientist’), Science Council, UK. • Elected Member, International Statistical Institute, NL. • Senior Member, American Society for Quality, USA. • President of the Swiss Statistical Society (2009-2015). Founder, CEO & CAO, Statoo Consulting, Switzerland (since 2001). Professor of Data Science, Research Center for Statistics (RCS), Geneva School of Economics and Management (GSEM), University of Geneva, Switzerland (since 2016). Founding Director of GSEM’s new MSc in Business Analytics program (started fall 2017).
    [Show full text]
  • ASTRONOMY 630 Numerical and Statistical Methods in Astrophysics
    Astr 630 Class Notes { Spring 2020 1 ASTRONOMY 630 Numerical and statistical methods in astrophysics CLASS NOTES Spring 2020 Instructor: Jon Holtzman Astr 630 Class Notes { Spring 2020 2 1 Introduction What is this class about? What is statistics? Read and discuss Feigelson & Babu 1.1.2 and 1.1.3. The use of statistics in astronomy may not be as controversial as it is in other fields, probably because astronomers feel that there is basic physics that underlies the phenomena we observe. What is data mining and machine learning? Read and discuss Ivesic 1.1 Astroinformatics What are some astronomy applications of statistics and data mining? Classifica- tion, parameter estimation, comparison of hypotheses, absolute evaluation of hypoth- esis, forecasting, finding substructure in data, finding corrrelations in data Some examples: • Classification: what is relative number of different types of planets, stars, galax- ies? Can a subset of observed properties of an object be used to classify the object, and how accurately? e.g., emission line ratios • Parameter estimation: Given a set of data points with uncertainties, what are slope and amplitude of a power-law fit? What are the uncertainties in the parameters? Note that this assumes that power-law description is valid. • Hypothesis comparison: Is a double power-law better than a single power-law? Note that hypothesis comparisons are trickier when the number of parameters is different, since one must decide whether the fit to the data is sufficiently bet- ter given the extra freedom in the more complex model. A simpler comparison would be single power-law vs.
    [Show full text]
  • Resampling Methods in Paleontology
    RESAMPLING METHODS IN PALEONTOLOGY MICHAŁ KOWALEWSKI Department of Geosciences, Virginia Tech, Blacksburg, VA 24061 and PHIL NOVACK-GOTTSHALL Department of Biology, Benedictine University, 5700 College Road, Lisle, IL 60532 ABSTRACT.—This chapter reviews major types of statistical resampling approaches used in paleontology. They are an increasingly popular alternative to the classic parametric approach because they can approximate behaviors of parameters that are not understood theoretically. The primary goal of most resampling methods is an empirical approximation of a sampling distribution of a statistic of interest, whether simple (mean or standard error) or more complicated (median, kurtosis, or eigenvalue). This chapter focuses on the conceptual and practical aspects of resampling methods that a user is likely to face when designing them, rather than the relevant math- ematical derivations and intricate details of the statistical theory. The chapter reviews the concept of sampling distributions, outlines a generalized methodology for designing resampling methods, summarizes major types of resampling strategies, highlights some commonly used resampling protocols, and addresses various practical decisions involved in designing algorithm details. A particular emphasis has been placed here on bootstrapping, a resampling strategy used extensively in quantitative paleontological analyses, but other resampling techniques are also reviewed in detail. In addition, ad hoc and literature-based case examples are provided to illustrate virtues, limitations, and potential pitfalls of resampling methods. We can formulate bootstrap simulations for almost any methods from a practical, paleontological perspective. conceivable problem. Once we program the computer In largely inductive sciences, such as biology or to carry out the bootstrap replications, we let the com- paleontology, statistical evaluation of empirical data, puter do all the work.
    [Show full text]
  • Factors Affecting the Accuracy of a Class Prediction Model in Gene Expression Data Putri W
    Novianti et al. BMC Bioinformatics (2015) 16:199 DOI 10.1186/s12859-015-0610-4 RESEARCH ARTICLE Open Access Factors affecting the accuracy of a class prediction model in gene expression data Putri W. Novianti1*, Victor L. Jong1,2, Kit C. B. Roes1 and Marinus J. C. Eijkemans1 Abstract Background: Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. Results: Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/ cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression.
    [Show full text]
  • Efficiency of Parameter Estimator of Various Resampling Methods on Warppls Analysis
    Mathematics and Statistics 8(5): 481-492, 2020 http://www.hrpub.org DOI: 10.13189/ms.2020.080501 Efficiency of Parameter Estimator of Various Resampling Methods on WarpPLS Analysis Luthfatul Amaliana, Solimun*, Adji Achmad Rinaldo Fernandes, Nurjannah Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia Received May 9, 2020; Revised July 16, 2020; Accepted July 29, 2020 Cite This Paper in the following Citation Styles (a): [1] Luthfatul Amaliana, Solimun, Adji Achmad Rinaldo Fernandes, Nurjannah , "Efficiency of Parameter Estimator of Various Resampling Methods on WarpPLS Analysis," Mathematics and Statistics, Vol. 8, No. 5, pp. 481 - 492, 2020. DOI: 10.13189/ms.2020.080501. (b): Luthfatul Amaliana, Solimun, Adji Achmad Rinaldo Fernandes, Nurjannah (2020). Efficiency of Parameter Estimator of Various Resampling Methods on WarpPLS Analysis. Mathematics and Statistics, 8(5), 481 - 492. DOI: 10.13189/ms.2020.080501. Copyright©2020 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract WarpPLS analysis has three algorithms, 1. Introduction namely the outer model parameter estimation algorithm, the inner model, and the hypothesis testing algorithm Structural Equation Modeling (SEM) is an analysis to which consists of several choices of resampling methods obtain data and relationships between latent variables that namely Stable1, Stable2, Stable3, Bootstrap, Jackknife, are carried out simultaneously [1]. PLS is a method that is and Blindfolding. The purpose of this study is to apply the more complicated than SEM because it can be applied to WarpPLS analysis by comparing the six resampling the reflective indicator model and the formative indicator methods based on the relative efficiency of the parameter model.
    [Show full text]
  • Application of Bootstrap Resampling Technique to Obtain Confidence Interval for Prostate Specific Antigen (PSA) Screening Age
    American Journal of www.biomedgrid.com Biomedical Science & Research ISSN: 2642-1747 --------------------------------------------------------------------------------------------------------------------------------- Research Article Copy Right@ Ijomah Maxwell Azubuike Application of Bootstrap Resampling Technique to Obtain Confidence Interval for Prostate Specific Antigen (PSA) Screening Age Ijomah, Maxwell Azubuike*, Chris-Chinedu, Joy Nonso Department of Maths & Statistics, Nigeria *Corresponding author: Ijomah, Maxwell Azubuike, Department of Maths & Statistics, Nigeria. To Cite This Article: Maxwell Azubuik. Application of Bootstrap Resampling Technique to Obtain Confidence Interval for Prostate Specific Antigen (PSA) Screening Age. 2020 - 9(2). AJBSR.MS.ID.001378. DOI: 10.34297/AJBSR.2020.09.001378. Received: May 22, 2020; Published: June 22, 2020 Abstract generallyProstate-specific favored because antigen their (PSA) use testing may delayis one the of the detection most commonly of prostate used cancer screening in many tools men to detect and as clinically such may significant lead to prostateharm in cancers at a stage when intervention reduces morbidity and mortality. However age-specific reference ranges have not been screening test in asymptomatic men is still subject to debate. In this article, we applied bootstrap resampling technique to obtain the terms of overdiagnosis and overtreatment. The widespread use of PSA has proven controversial as the evidence for benefit as a investigating variations among selected models in samples
    [Show full text]
  • Arxiv:2101.07256V1 [Physics.Data-An] 15 Jan 2021 Nomials, Fourier Modes, Wavelets, Or Spherical Harmonics
    Fitting very flexible models: Linear regression with large numbers of parameters1 David W. Hogg Flatiron Institute, a division of the Simons Foundation Center for Cosmology and Particle Physics, Department of Physics, New York University Center for Data Science, New York University Max-Planck-Institut f¨urAstronomie, Heidelberg Soledad Villar Department of Applied Mathematics & Statistics, Johns Hopkins University Mathematical Institute for Data Science, Johns Hopkins University Abstract: There are many uses for linear fitting; the context here is interpo- lation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function to those data. Or you want to fit a flexible function to de-trend a time series or normalize a spectrum. In these contexts, investigators often choose a polynomial basis, or a Fourier basis, or wavelets, or something equally general. They also choose an order, or number of ba- sis functions to fit, and (often) some kind of regularization. We discuss how this basis-function fitting is done, with ordinary least squares and extensions thereof. We emphasize that it is often valuable to choose far more parameters than data points, despite folk rules to the contrary: Suitably regularized models with enormous numbers of parameters generalize well and make good predic- tions for held-out data; over-fitting is not (mainly) a problem of having too many parameters. It is even possible to take the limit of infinite parameters, at which, if the basis and regularization are chosen correctly, the least-squares fit becomes the mean of a Gaussian process. We recommend cross-validation as a good empirical method for model selection (for example, setting the number of parameters and the form of the regularization), and jackknife resampling as a good empirical method for estimating the uncertainties of the predictions made by the model.
    [Show full text]
  • An Unbiased Variance Estimator of a K-Sample U-Statistic with Application to AUC in Binary Classification
    An Unbiased Variance Estimator of a K-sample U-statistic with Application to AUC in Binary Classification Abstract: Many questions in research can be rephrased as binary classification tasks, to find simple yes-or-no answers. For classifiers trained to answer these queries, area under the ROC (receiver operating characteristic) curve (AUC) is a popular metric for assessing the performance of a binary classification method. However, due to sampling variation, the model with the largest AUC score for a given data set is not necessarily the optimal model. Thus, it is important to evaluate the variance of AUC. We first recognize that AUC can be estimated unbiasedly in the form of a two- sample U-statistic. We then propose a new method, an unbiased variance estimator of a general K-sample U-statistic, and apply it to evaluating the variance of AUC. To realize the proposed unbiased variance estimator of AUC, we propose to use a partition resampling scheme that yields high computational efficiency. We conduct simulation studies to investigate the performance of the developed method in comparison to bootstrap and jackknife variance estimators. The simulations suggest that the proposal yields comparable or even better results in terms of bias and mean squared error. In addition, it has significantly improved computational efficiency compared to its resampling-based counterparts. Moreover, we also discuss the generalization of the devised method to estimating the variance of a general K-sample U-statistic (K 2), which has broad applications ≥ in practice. 2 1. Introduction Classification is one of the pattern recognition problems in statistics, where the larger task of pattern recognition uses algorithms to identify regularities in the data and creates a mapping from a given set of input values to an output space (Bishop, 2006).
    [Show full text]