Log in

Relevant bibliographies by topics / Linear classification methods / Journal articles

To see the other types of publications on this topic, follow the link: Linear classification methods.

Journal articles on the topic 'Linear classification methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Linear classification methods.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Zhou, Jianhang, Shaoning Zeng, and Bob Zhang. "Linear Representation-Based Methods for Image Classification: A Survey." IEEE Access 8 (2020): 216645–70. http://dx.doi.org/10.1109/access.2020.3041154.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Nikolov, Hristo S., Doyno I. Petkov, Nina Jeliazkova, Stela Ruseva, and Kiril Boyanov. "Non-linear methods in remotely sensed multispectral data classification." Advances in Space Research 43, no. 5 (2009): 859–68. http://dx.doi.org/10.1016/j.asr.2008.06.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Byrnes, Christopher I., and Peter E. Crouch. "Geometric methods for the classification of linear feedback systems." Systems & Control Letters 6, no. 4 (1985): 239–46. http://dx.doi.org/10.1016/0167-6911(85)90074-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Schmah, Tanya, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, and Stephen C. Strother. "Comparing Classification Methods for Longitudinal fMRI Studies." Neural Computation 22, no. 11 (2010): 2729–62. http://dx.doi.org/10.1162/neco_a_00024.

Full text

Abstract:

We compare 10 methods of classifying fMRI volumes by applying them to data from a longitudinal study of stroke recovery: adaptive Fisher's linear and quadratic discriminant; gaussian naive Bayes; support vector machines with linear, quadratic, and radial basis function (RBF) kernels; logistic regression; two novel methods based on pairs of restricted Boltzmann machines (RBM); and K-nearest neighbors. All methods were tested on three binary classification tasks, and their out-of-sample classification accuracies are compared. The relative performance of the methods varies considerably across sub

APA, Harvard, Vancouver, ISO, and other styles

5

Taskin, A. S., E. M. Mirkes, and N. Y. Sirotinina. "Application of the Fuzzy Classification for Linear Hybrid Prediction Methods." Modeling and Analysis of Information Systems 20, no. 3 (2015): 108–20. http://dx.doi.org/10.18255/1818-1015-2013-3-108-120.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Hadad, Yossi, and Baruch Keren. "ABC inventory classification via linear discriminant analysis and ranking methods." International Journal of Logistics Systems and Management 14, no. 4 (2013): 387. http://dx.doi.org/10.1504/ijlsm.2013.052744.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Ştefan, Raluca-Mariana, Măriuţa Şerban, Iulian-Ion Hurloiu, and Bianca-Florentina Rusu. "Kernel Methods for Data Classification." International conference KNOWLEDGE-BASED ORGANIZATION 22, no. 3 (2016): 572–75. http://dx.doi.org/10.1515/kbo-2016-0098.

Full text

Abstract:

Abstract In the past decades, the exponential evolution of data collection for macroeconomic databases in digital format caused a huge increase in their volume. As a consequence, the automatic organization and the classification of macroeconomic data show a significant practical value. Various techniques for categorizing data are used to classify numerous macroeconomic data according to the classes they belong to. Since the manual construction of some of the classifiers is difficult and time consuming, are preferred classifiers that learn from action examples, a process which forms the supervi

APA, Harvard, Vancouver, ISO, and other styles

8

HSIEH, PI-FUEI, MING-HUA YANG, YI-JAY GU, and YU-CHENG LIANG. "CLASSIFICATION-ORIENTED LOCALLY LINEAR EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (2010): 737–62. http://dx.doi.org/10.1142/s0218001410008159.

Full text

Abstract:

The locally linear embedding (LLE) algorithm is hypothetically able to find a lower dimensional space than a linear method for preserving a data manifold originally embedded in a high dimensional space. However, uneven sampling over the manifold in real-world data ultimately causes LLE to suffer from the disconnected-neighborhood problem. Consequently, the final dimensionality required for the data manifold is multiplied by the number of disjoint groups in the complete data representation. In addition, LLE as an unsupervised method is unable to suppress between-class connections. This means th

APA, Harvard, Vancouver, ISO, and other styles

9

Puengnim, Anchalee, Nathalie Thomas, Jean-Yves Tourneret, and Josep Vidal. "Classification of linear and non-linear modulations using the Baum–Welch algorithm and MCMC methods." Signal Processing 90, no. 12 (2010): 3242–55. http://dx.doi.org/10.1016/j.sigpro.2010.05.030.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Mohanavalli, S., S. Karthika, Srividya ., K. R.Uthayan, and N. Sandya. "Categorisation of Tweets Using Ensemble Classification Methods." International Journal of Engineering & Technology 7, no. 3.12 (2018): 722. http://dx.doi.org/10.14419/ijet.v7i3.12.16463.

Full text

Abstract:

Twitter is a micro-blogging site that facilitates users to exchange short messages. Twitter is predominantly used in fields like business, healthcare, education and nation security. Twitter is being used by a large number of users for updating real time information and sentiment expression. The objective of this paper is to automate the classification of tweets into particular category using various machine learning algorithms like naïve bayes, SVM, and linear regression model. The proposed ensemble model aims to improve performance metrics of these algorithms. A comparative study of the algor

APA, Harvard, Vancouver, ISO, and other styles

11

Okwonu, Friday Zinzendoff, and Abdul Rahman Othman. "Heteroscedastic variance covariance matrices for unbiased two groups linear classification methods." Applied Mathematical Sciences 7 (2013): 6855–65. http://dx.doi.org/10.12988/ams.2013.39486.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Nawaz Jadoon, Rab, Waqas Jadoon, Ahmad Khan, et al. "Linear Discriminative Learning for Image Classification." Mathematical Problems in Engineering 2019 (October 20, 2019): 1–12. http://dx.doi.org/10.1155/2019/4760614.

Full text

Abstract:

In this paper, we propose a linear discriminative learning model called adaptive locality-based weighted collaborative representation (ALWCR) that formulates the image classification task as an optimization problem to reduce the reconstruction error between the query sample and its computed linear representation. The optimal linear representation for a query image is obtained by using the weighted regularized linear regression approach which incorporates intrinsic locality structure and feature variance between data into representation. The resultant representation increases the discrimination

APA, Harvard, Vancouver, ISO, and other styles

13

Zhou, Ming, Shu He, and Yong Jun Cheng. "The Application of Kernel Methods for Image Classification." Advanced Materials Research 1044-1045 (October 2014): 1388–91. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1388.

Full text

Abstract:

Kernel methods are famous for their efficiency and robustness in processing non-linear machine learning problems in the high dimensional feature space, and thus widely applied in image classification and detection. The proper principal components are selected for KPCA reconstruction according to noise features. Finally, the improved image is obtained by performing inverse method. Experimental results show that the proposed method can suppress noise interference in remote sensing images, and preserve the useful information of original data more completely.

APA, Harvard, Vancouver, ISO, and other styles

14

Kapralov, Nikolai, Zhanna Nagornova, and Natalia Shemyakina. "Classification Methods for EEG Patterns of Imaginary Movements." Informatics and Automation 20, no. 1 (2021): 94–132. http://dx.doi.org/10.15622/ia.2021.20.1.4.

Full text

Abstract:

The review focuses on the most promising methods for classifying EEG signals for non-invasive BCIs and theoretical approaches for the successful classification of EEG patterns. The paper provides an overview of articles using Riemannian geometry, deep learning methods and various options for preprocessing and "clustering" EEG signals, for example, common-spatial pattern (CSP). Among other approaches, pre-processing of EEG signals using CSP is often used, both offline and online. The combination of CSP, linear discriminant analysis, support vector machine and neural network (BPNN) made it possi

APA, Harvard, Vancouver, ISO, and other styles

15

Garrett, D., D. A. Peterson, C. W. Anderson, and M. H. Thaut. "Comparison of linear, nonlinear, and feature selection methods for eeg signal classification." IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, no. 2 (2003): 141–44. http://dx.doi.org/10.1109/tnsre.2003.814441.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Gou, Jianping, Jun Song, Weihua Ou, Shaoning Zeng, Yunhao Yuan, and Lan Du. "Representation-based classification methods with enhanced linear reconstruction measures for face recognition." Computers & Electrical Engineering 79 (October 2019): 106451. http://dx.doi.org/10.1016/j.compeleceng.2019.106451.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Japkowicz, Nathalie, Stephen José Hanson, and Mark A. Gluck. "Nonlinear Autoassociation Is Not Equivalent to PCA." Neural Computation 12, no. 3 (2000): 531–45. http://dx.doi.org/10.1162/089976600300015691.

Full text

Abstract:

A common misperception within the neural network community is that even with nonlinearities in their hidden layer, autoassociators trained with backpropagation are equivalent to linear methods such as principal component analysis (PCA). Our purpose is to demonstrate that nonlinear autoassociators actually behave differently from linear methods and that they can outperform these methods when used for latent extraction, projection, and classification. While linear autoassociators emulate PCA, and thus exhibit a flat or unimodal reconstruction error surface, autoassociators with nonlinearities in

APA, Harvard, Vancouver, ISO, and other styles

18

Yang, Chun, Jinyi Long, and Hao Wang. "Performance Comparison of Classification Methods for Surface EMG-Based Human-Machine Interface." International Journal of Grid and High Performance Computing 7, no. 4 (2015): 47–56. http://dx.doi.org/10.4018/ijghpc.2015100104.

Full text

Abstract:

Reliable control of assistive devices through surface electromyography (sEMG) based human-machine interfaces (HMIs) requires accurate classification of multi-channel sEMG. The design of effective pattern classification methods is one of the main challenges for sEMG-based HMIs. In this paper, the authors compared comprehensively the performance of different linear and nonlinear classifiers for the pattern classification of sEMG with respect to three pairs of upper-limb motions (i.e., hand close vs. hand open, wrist flexion vs. wrist extension, and forearm pronation vs. forearm supination). A fe

APA, Harvard, Vancouver, ISO, and other styles

19

Hussain, Zahraa Faiz, Hind Raad Ibraheem, Mohammad Alsajri, et al. "A new model for iris data set classification based on linear support vector machine parameter's optimization." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 1079. http://dx.doi.org/10.11591/ijece.v10i1.pp1079-1084.

Full text

Abstract:

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary proble

APA, Harvard, Vancouver, ISO, and other styles

20

Finch, Holmes, and Mercedes K. Schneider. "Classification Accuracy of Neural Networks vs. Discriminant Analysis, Logistic Regression, and Classification and Regression Trees." Methodology 3, no. 2 (2007): 47–57. http://dx.doi.org/10.1027/1614-2241.3.2.47.

Full text

Abstract:

Abstract. This paper compares the predictive accuracy of three commonly used parametric methods for group classification, linear discriminant analysis, quadratic discriminant analysis, and logistic regression, with two less common approaches, neural networks and classification and regression trees. The simulation study examined the impact of such factors as inequality of covariance matrices, distribution of predictors, and group size ratio (among others) on the performance of each method. Results indicate that quadratic discriminant analysis always performs as well as the other methods while n

APA, Harvard, Vancouver, ISO, and other styles

21

Jia, Cangzhi, Hongyan Gong, Yan Zhu, and Yixia Shi. "The Computational Prediction Methods for Linear B-cell Epitopes." Current Bioinformatics 14, no. 3 (2019): 226–33. http://dx.doi.org/10.2174/1574893613666181112145706.

Full text

Abstract:

Background: B-cell epitope prediction is an essential tool for a variety of immunological studies. For identifying such epitopes, several computational predictors have been proposed in the past 10 years. Objective: In this review, we summarized the representative computational approaches developed for the identification of linear B-cell epitopes. </P><P> Methods: We mainly discuss the datasets, feature extraction methods and classification methods used in the previous work. Results: The performance of the existing methods was not very satisfying, and so more effective approaches sh

APA, Harvard, Vancouver, ISO, and other styles

22

Abdolmaleki, Azizeh, Jahan Ghasemi, Fereshteh Shiri, and Somayeh Pirhadi. "Application of Multivariate Linear and Nonlinear Calibration and Classification Methods in Drug Design." Combinatorial Chemistry & High Throughput Screening 18, no. 8 (2015): 795–808. http://dx.doi.org/10.2174/1386207318666150803142158.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Stam, Antonie, and Erich A. Joachimsthaler. "Solving the Classification Problem in Discriminant Analysis Via Linear and Nonlinear Programming Methods." Decision Sciences 20, no. 2 (1989): 285–93. http://dx.doi.org/10.1111/j.1540-5915.1989.tb01878.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Ayaz, Hamail, Erick Rodríguez-Esparza, Muhammad Ahmad, Diego Oliva, Marco Pérez-Cisneros, and Ram Sarkar. "Classification of Apple Disease Based on Non-Linear Deep Features." Applied Sciences 11, no. 14 (2021): 6422. http://dx.doi.org/10.3390/app11146422.

Full text

Abstract:

Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using d

APA, Harvard, Vancouver, ISO, and other styles

25

AFZAL, WASIF, RICHARD TORKAR, and ROBERT FELDT. "RESAMPLING METHODS IN SOFTWARE QUALITY CLASSIFICATION." International Journal of Software Engineering and Knowledge Engineering 22, no. 02 (2012): 203–23. http://dx.doi.org/10.1142/s0218194012400037.

Full text

Abstract:

In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use. This is seen as one of the contributing factors for not being able to draw general conclusions on what modeling technique or set

APA, Harvard, Vancouver, ISO, and other styles

26

Hloušková, Zuzana, and Marie Prášilová. "Classification of Specialized Farms Applying Multivariate Statistical Methods." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, no. 3 (2017): 1007–14. http://dx.doi.org/10.11118/actaun201765031007.

Full text

Abstract:

Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agricultu

APA, Harvard, Vancouver, ISO, and other styles

27

Wang, Shi Long, and Hua Wang. "Classification of Mobility of Cellular Phone Using Linear Classification and k-Clustering." Advanced Engineering Forum 5 (July 2012): 3–8. http://dx.doi.org/10.4028/www.scientific.net/aef.5.3.

Full text

Abstract:

Road traffic data is a fundamental element of intelligent traffic system. However, due to the high investment of the road sensor, the availability of the traffic data is so limited that it can’t satisfy the requirement of current situation. Using cellular phone information as road traffic data becomes an attractive alternative because of its low cost, widespread and high cover rate. Until now, there are several algorithms to process the cellular phone information and most of them present promising conclusion. However, in order to get the satisfying conclusion, nearly all of these methods depen

APA, Harvard, Vancouver, ISO, and other styles

28

Maiti, Tapabrata, and Pushpal Mukhopadhyay. "Comparison of Statistical Classification Methods Based on a Prostate Cancer Study." Calcutta Statistical Association Bulletin 57, no. 3-4 (2005): 219–38. http://dx.doi.org/10.1177/0008068320050306.

Full text

Abstract:

Prostate cancer is one of the most common cancers in American men. Management of prostate cancer depends on its stage, because only cancers that are confined to the organ of origin are potentially curable by radical prostatectomy. In this article we have considered different statistical methods to predict the probabilities of nonorgan confined prostate cancer based on its clinical stage. Modern computer intensive methods such as bagging, neural networks and support vector machines are compared to more classical methods such as linear, quadratic and logistic discrimination and less computer in

APA, Harvard, Vancouver, ISO, and other styles

29

Wang, Zhen, and Dong Mei Li. "Multiple-Instance Classification via Generalized Eigenvalue Proximal SVM." Advanced Materials Research 143-144 (October 2010): 1235–39. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1235.

Full text

Abstract:

The multiple-instance classification problem is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite dimensional real space subject to linear and bilinear constraints by SVM-based methods. This paper presents a new multiple-instance classifier that determines two nonparallel planes by solving generalized eigenvalue proximal SVM. Our method converges in a few iterations to a local solution. Computational results on a number of datasets indicate that the proposed algorithm is competitive with the other SVM-based methods in multiple-instance classific

APA, Harvard, Vancouver, ISO, and other styles

30

Manyakov, Nikolay V., Nikolay Chumerin, Adrien Combaz, and Marc M. Van Hulle. "Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/519868.

Full text

Abstract:

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the

APA, Harvard, Vancouver, ISO, and other styles

31

Тебекин, А., and A. Tebekin. "Adoption of management decisions based on programming methods as a subgroup of methods for optimizing performance indicators." Journal of Management Studies 4, no. 9 (2019): 34–44. http://dx.doi.org/10.12737/article_5d68d583605411.06206955.

Full text

Abstract:

The author's classification of management decision-making methods, including twenty-five classes of methods, is presented for the first time. As part of the general classification of management decision-making methods, the role and place of a group of methods for making managerial decisions based on the optimization of performance indicators was demonstrated. In the group of methods for making managerial decisions based on the optimization of performance indicators, a subgroup of programming methods (linear, nonlinear and dynamic) is considered in detail. The features of use and application ar

APA, Harvard, Vancouver, ISO, and other styles

32

Dossat, Nadège, Alain Mangé, Jérôme Solassol, et al. "Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis." Cancer Informatics 3 (January 2007): 117693510700300. http://dx.doi.org/10.1177/117693510700300023.

Full text

Abstract:

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spe

APA, Harvard, Vancouver, ISO, and other styles

33

Andrecut, M. "Randomized kernel methods for least-squares support vector machines." International Journal of Modern Physics C 28, no. 02 (2017): 1750015. http://dx.doi.org/10.1142/s0129183117500152.

Full text

Abstract:

The least-squares support vector machine (LS-SVM) is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the LS-SVM classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.

APA, Harvard, Vancouver, ISO, and other styles

34

Arefi Shirvan, Reza, Seyed Kamaledin Setarehdan, and Ali Motie Nasrabadi. "Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features." International Clinical Neuroscience Journal 5, no. 2 (2018): 55–61. http://dx.doi.org/10.15171/icnj.2018.11.

Full text

Abstract:

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health. Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After e

APA, Harvard, Vancouver, ISO, and other styles

35

Patgiri, Chayashree, Mousmita Sarma, and Kandarpa Kumar Sarma. "A Class of Neuro-Computational Methods for Assamese Fricative Classification." Journal of Artificial Intelligence and Soft Computing Research 5, no. 1 (2015): 59–70. http://dx.doi.org/10.1515/jaiscr-2015-0019.

Full text

Abstract:

Abstract In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained

APA, Harvard, Vancouver, ISO, and other styles

36

Alrawashdeh, Mufda Jameel, Taha Radwan Radwan, and Kalid Abunawas Abunawas. "Performance of Linear Discriminant Analysis Using Different Robust Methods." European Journal of Pure and Applied Mathematics 11, no. 1 (2018): 284. http://dx.doi.org/10.29020/nybg.ejpam.v11i1.3176.

Full text

Abstract:

This study aims to combine the new deterministic minimum covariance determinant (DetMCD) algorithm with linear discriminant analysis (LDA) and compare it with the fast minimum covariance determinant (FastMCD), fast consistent high breakdown (FCH), and robust FCH (RFCH) algorithms. LDA classifies new observations into one of the unknown groups and it is widely used in multivariate statistical analysis. The LDA mean and covariance matrix parameters are highly influenced by outliers. The DetMCD algorithm is highly robust and resistant to outliers and it is constructed to overcome the outlier prob

APA, Harvard, Vancouver, ISO, and other styles

37

Buchala, Samarasena, Neil Davey, Tim M. Gale, and Ray J. Frank. "Analysis of linear and nonlinear dimensionality reduction methods for gender classification of face images." International Journal of Systems Science 36, no. 14 (2005): 931–42. http://dx.doi.org/10.1080/00207720500381573.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Kouskoumvekaki, Irene, Zhiyong Yang, Svava Ó. Jónsdóttir, Lisbeth Olsson, and Gianni Panagiotou. "Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification." BMC Bioinformatics 9, no. 1 (2008): 59. http://dx.doi.org/10.1186/1471-2105-9-59.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Imran, Farid Muhammad, Mingyi He, and Yifan Zhang. "Systematic Comparison of Linear Feature Extraction Methods for Classification of Hyperspectral Images with Noises." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 9 (2015): 13–20. http://dx.doi.org/10.14257/ijsip.2015.8.9.02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Çiğdem TURHAL. "Plant Identification Via Leaf Classification Using Color and Biometric Features." ISPEC Journal of Agricultural Sciences 5, no. 2 (2021): 393–400. http://dx.doi.org/10.46291/ispecjasvol5iss2pp393-400.

Full text

Abstract:

Plants that are of great importance for humans and other living things are an integral part of our ecosystem. In today's world, where many plant species are at risk of disappearance, the identification of plants helps to protect and survive all natural life. There are many studies presented in the literature for plant identification. The most popular of these identification methods is leaf based classification. The reason for choosing leaves in this classification is that they are easier to obtain than other biometric components such as flowers available for a short period of time. Various bio

APA, Harvard, Vancouver, ISO, and other styles

41

Suhandy, Diding, and Meinilwita Yulia. "Luwak Coffee Classification Using UV-Vis Spectroscopy Data: Comparison of Linear Discriminant Analysis and Support Vector Machine Methods." Aceh International Journal of Science and Technology 7, no. 2 (2018): 115–21. http://dx.doi.org/10.13170/aijst.7.2.8972.

Full text

Abstract:

UV-Vis spectroscopy has been used as a promising method for coffee quality evaluation including in authentication of several high-economic coffee types. In this paper, we have compared the abilities of linear discriminant analysis (LDA) and support vector machines classification (SVMC) methods for Luwak coffee classification. UV-Vis spectral data of 50 samples of pure Luwak coffee and 50 samples of pure non-Luwak coffee were acquired using a UV-Vis spectrometer in transmittance mode. The results show that UV-Vis spectroscopy combined with LDA and SVMC was an effective method to classify Luwak

APA, Harvard, Vancouver, ISO, and other styles

42

Eldén, Lars. "Numerical linear algebra in data mining." Acta Numerica 15 (May 2006): 327–84. http://dx.doi.org/10.1017/s0962492906240017.

Full text

Abstract:

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

APA, Harvard, Vancouver, ISO, and other styles

43

Tripathi, Alpika, Geetika Srivastava, K. K. Singh, and P. K. Maurya. "Epileptic Seizure Data Classification Using RBAs and Linear SVM." Biomedical and Pharmacology Journal 12, no. 2 (2019): 549–62. http://dx.doi.org/10.13005/bpj/1674.

Full text

Abstract:

The objective of this paper is to make a distinction between EEG data of normal and epileptic subjects. Methods: The dataset is taken from 20-30 years healthy male/female subjects from EEG lab of Dept. of Neurology, Dr. RML Institute of Medical Sciences, Lucknow (India). The feature extraction has been done using the Hilbert Huang Transform (HHT) method. The experimental EEG signals have been decomposed till 5th level of Intrinsic Mode Function (IMF) followed by calculation of high order statistical values of each IMF. Relief algorithm (RBAs) is used for feature selection and classification is

APA, Harvard, Vancouver, ISO, and other styles

44

Qi, Ren, Anjun Ma, Qin Ma, and Quan Zou. "Clustering and classification methods for single-cell RNA-sequencing data." Briefings in Bioinformatics 21, no. 4 (2019): 1196–208. http://dx.doi.org/10.1093/bib/bbz062.

Full text

Abstract:

Abstract Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. W

APA, Harvard, Vancouver, ISO, and other styles

45

Kowsari, Jafari Meimandi, Heidarysafa, Mendu, Barnes, and Brown. "Text Classification Algorithms: A Survey." Information 10, no. 4 (2019): 150. http://dx.doi.org/10.3390/info10040150.

Full text

Abstract:

In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief

APA, Harvard, Vancouver, ISO, and other styles

46

Lee, Ching-Pei, and Chih-Jen Lin. "Large-Scale Linear RankSVM." Neural Computation 26, no. 4 (2014): 781–817. http://dx.doi.org/10.1162/neco_a_00571.

Full text

Abstract:

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discu

APA, Harvard, Vancouver, ISO, and other styles

47

Rocha, Werickson Fortunato de Carvalho, Charles Bezerra do Prado, and Niksa Blonder. "Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods." Molecules 25, no. 13 (2020): 3025. http://dx.doi.org/10.3390/molecules25133025.

Full text

Abstract:

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss cri

APA, Harvard, Vancouver, ISO, and other styles

48

Szabó, Loránd, Dávid Abriha, Kwanele Phinzi, and Szilárd Szabó. "Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery." Landscape & Environment 15, no. 1 (2021): 66–75. http://dx.doi.org/10.21120/le/15/1/9.

Full text

Abstract:

In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation stru

APA, Harvard, Vancouver, ISO, and other styles

49

Cannings, Timothy I., Yingying Fan, and Richard J. Samworth. "Classification with imperfect training labels." Biometrika 107, no. 2 (2020): 311–30. http://dx.doi.org/10.1093/biomet/asaa011.

Full text

Abstract:

Summary We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we deri

APA, Harvard, Vancouver, ISO, and other styles

50

Izadi, Bahram, Bahram Ranjbarian, Saeedeh Ketabi, and Faria Nassiri-Mofakham. "Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection." International Journal of Information Technology and Computer Science 5, no. 10 (2013): 9–20. http://dx.doi.org/10.5815/ijitcs.2013.10.02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!