Pasupa, Kitsuchart
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Pasupa, Kitsuchart
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kitsuchart.pa@kmitl.ac.th
51 results
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Item type:Publication, Text-background decomposition for thai text localization and recognition in natural scenes(2014-01-12); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An approach to face shape classification for hairstyle recommendation(2016-04-07) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analytical incremental learning: Fast constructive learning method for neural network(2016-01-01); Extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feed-forward neural network (SLFN) based on random input weights which usually requires large number of hidden nodes. Recently, novel constructive and destructive parsimonious (CP and DP)-ELM which provide the effectiveness generalization and compact hidden nodes have been proposed. However, the performance might be unstable due to the randomization either in ordinary ELM or CP and DP-ELM. In this study, analytical incremental learning (AIL) algorithm is proposed in which all weights of neural network are calculated analytically without any randomization. The hidden nodes of AIL are incrementally generated based on residual error using least square (LS) method. The results show the effectiveness of AIL which has not only smallest number of hidden nodes and more stable but also good generalization than those of ELM, CP and DP-ELM based on seven benchmark data sets evaluation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sparse Fisher discriminant analysis with Jeffrey's hyperprior(2012-12-01)The penalty function requires a choice of regularization parameter which controls the degree of parsimony in sparse kernel classifier. This involves an extra parameter apart from kernel parameter in the optimization which must be found via, e.g. cross-validation. This paper introduces a new parsimonious binary kernel Fisher discriminant analysis which does not require a regularization parameter. This can be done by using a Jeffrey's noninformative hyperprior. A Jeffrey's noninformative hyperprior is parameter-free and is adopted through a hierarchical-Bayes interpretation of the Laplacian prior distribution. This leads to a non-requirement of the regularization parameter. The proposed algorithm is compared with other machine learning methods on substantial benchmarks. Moreover, it is also compared with the leading machine learning in virtual screening application. It is found to be less accurate but it is still comparable in a number of cases. © 2012 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Learning to predict where people look with tensor-based multi-view learning(2015-01-01); Szedmak, SandorEye movements data collection is very expensive and laborious. Moreover, there are usually missing values. Assuming that we are collecting eye movements data on a set of images from different users (views). There is a possibility that we are not able to collect eye movements of all users on all images. One or more views are not represented in the image. We assume that the relationships among the views can be learnt from the complete items. The task is then to reproduce the missing part of the incomplete items from the relationships derived from the complete items and the known part of these items. Using the properties of tensor algebra we show that this problem can be formulated consistently as a regression type learning task. Furthermore, there is a maximum margin based optimisation framework where this problem can be solved in a tractable way. This problem is similar to learning to predict where human look. The proposed algorithm is proved to be more effective than well-known saliency detection techniques. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Thai Sentiment Analysis via Bidirectional LSTM-CNN Model with Embedding Vectors and Sentic Features(2018-07-02) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A coefficient comparison of weighted similarity extreme learning machine for drug screening(2016-03-23); Machine learning techniques are becoming popular in drug discovery process. It can be used to predict the biological activities of compounds. This paper focuses on virtual screening task. We proposed the Weighted Similarity Extreme Learning Machine algorithm (WELM). It is based on Single Layer Feedforward Neural Network. The algorithm is powerful, iteratively free, and easy to program. In this work, we compared the performance of 17 different types of coefficients with WELM on a well-known dataset in the area of virtual screening named Maximum Unbiased Validation dataset. Moreover, the WELM with different types of coefficients were also compared with the conventional technique-similarity searching. WELM together with Jaccard/Tanimoto were able to achieve the best results on average in most of the activity classes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Combining multiple features for product categorisation by multiple kernel learning(2019-01-01); Hardoon, David R.E-commerce provides convenience and flexibility for consumers; for example, they can inquire about the availability of a desired product and get immediate response, hence they can seamlessly search for any desired products. Every day, e-commerce sites are updated with thousands of new images and their associated metadata (textual information), causing a problem of big data. Retail product categorisation involves cross-modal retrieval that shows the path of a category. In this study, we leveraged both image vectors of various aspects and textual metadata as features, then constructed a set of kernels. Multiple Kernel Learning (MKL) proposes to combine these kernels in order to achieve the best prediction accuracy. We compared the Support Vector Machine (SVM) prediction results between using an individual feature kernel and an MKL combined feature kernel to demonstrate the prediction improvement gained by MKL. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hinge loss projection for classification(2016-01-01); ; Sugimoto, MasanoriHinge loss is one-sided function which gives optimal solution than that of squared error (SE) loss function in case of classification. It allows data points which have a value greater than 1 and less than −1 for positive and negative classes, respectively. These have zero contribution to hinge function. However, in the most classification tasks, least square (LS) method such as ridge regression uses SE instead of hinge function. In this paper, a simple projection method is used to minimize hinge loss function through LS methods. We modify the ridge regression and its kernel based version i.e. kernel ridge regression so that it can adopt to hinge function instead of using SE in case of classification problem. The results show the effectiveness of hinge loss projection method especially on imbalanced data sets in terms of geometric mean (GM). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detecting Text in Manga Using Stroke Width Transform(2019-04-10); Sugimoto, MasanoriThe Japanese comic-book style known as manga is becoming a popular topic for researchers. This paper focuses on the problem of detecting text regions in manga pages. Because it is time-consuming and laborious to identify the text regions in images manually, an automatic approach is highly desirable. Here, we propose a new text-detection method for manga using a Stroke Width Transform (SWT) technique in conjunction with a Support Vector Machine (SVM). Conventional SWT-based text-detection techniques perform poorly with manga because both text and non-Text objects have similar characteristics for strokes, lines, and shapes. To better suit manga, we propose modifying the rules for finding letter candidates, which improves the ability to capture text. An SVM is then used to classify image patches into letter and nonletter regions. We compared our proposed framework with a conventional framework and other text-detection methods including deep-learning techniques. In the results, our proposed method achieved the highest F-measure of 0.506.