Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Time-series clustering - A decade review

International Joint Conference on Neural Networks

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Dynamic kernel (DK)-based support vector machines are used for the classification of varying length patterns. This paper explores the use of intermediate matching kernel (IMK) as a DK for classification of varying length patterns of long duration speech represented as sets of feature vectors. The main issue in construction of IMK is the choice for the set of virtual feature vectors used to select the local feature vectors for matching. This paper proposes to use components of class-independent Gaussian mixture model (CIGMM) as a representation for the set of virtual feature vectors. For every component of CIGMM, a local feature vector each from the two sets of local feature vectors that has the highest probability of belonging to that component is selected and a base kernel is computed between the selected local feature vectors. The IMK is computed as the sum of all the base kernels corresponding to different components of CIGMM. It is proposed to use the responsibility term weighted base kernels in computation of IMK to improve its discrimination ability. This paper also proposes the posterior probability weighted DKs (including the proposed IMKs) to improve their classification performance and reduce the number of support vectors. The performance of the support vector machine (SVM)-based classifiers using the proposed IMKs is studied for speech emotion recognition and speaker identification tasks and compared with that of the SVM-based classifiers using the state-of-the-art DKs.

GMM-based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines.

Classification models based on multilayer perceptron (MLP) or support vector machine (SVM) have been commonly used for complex pattern classification tasks. These models are suitable for classification of fixed dimension patterns. However, durations of consonant-vowel (CV) utterances vary not only for different classes, but also for a particular CV class. It is necessary to develop a method for representing the CV utterances by patterns of fixed dimension. For CV utterances, vowel onset point (VOP) is the instant at which the consonant part ends and the vowel part begins. Important information necessary for classification of CV utterances is present in the region around the VOP. A segment of fixed duration around the VOP can be processed to extract a pattern of fixed dimension to represent a CV utterance. Accurate detection of vowel onset points is important for recognition of CV utterances of speech. In this paper, we propose an approach for detection of VOP, based on dynamic time alignment between a reference pattern of a CV class and the pattern of an utterance of that class. The results of studies show that the hypothesised VOPs using the proposed approach have less deviation from their actual locations.

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Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances

Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear combination of base kernels, weighted according to the relevance of representation or feature. The MKL approach helps to select and combine the representations as well as to select features from a representation. Issues in the MKL algorithm are addressed in the framework of support vector machines (SVM). Studies on the representation and feature selection are presented for an image categorization task.

Representation and feature selection using multiple kernel learning

Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are multivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method.

A density based method for multivariate time series clustering in kernel feature space

In this paper, we propose the large margin autoregressive (LMAR) model for classification of time series patterns The parameters of the generative AR models for different classes are estimated using the margin of the boundaries of AR models as the optimization criterion Models that use a mixture of AR (MAR) models are considered for representing the data that cannot be adequately represented using a single AR model for a class Based on a mixture model representing each class, we propose the large margin mixture of AR (LMMAR) models The proposed methods are applied on the simulated time series data, electrocardiogram data, speech data for E-set in English alphabet and electroencephalogram time series data Performance of the proposed methods is compared with that of support vector machine (SVM) based classifier that uses AR coefficients based features The proposed methods give a better classification performance compared to the SVM based classifier Being generative models, the LMAR and LMMAR models provide a generative interpretation that enables utilization of the rejection option in the high risk classification tasks The proposed methods can also be used for detection of novel time series data

C. Chandra Sekhar

Papers

GMM-based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines.

Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances

Representation and feature selection using multiple kernel learning

A density based method for multivariate time series clustering in kernel feature space

Large margin mixture of AR models for time series classification