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On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice

Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on the categorization performance of subjects, that can isolate the information used. To this end, we developed Bubbles, a general technique that can assign the credit of human categorization performance to specific visual information. To illustrate the technique, we applied Bubbles on three categorization tasks (gender, expressive or not and identity) on the same set of faces, with human and ideal observers to compare the features they used.

Bubbles: A Technique to Reveal the Use of Information in Recognition Tasks EOA Report

A comprehensive survey on support vector machine classification: Applications, challenges and trends

http://library.um.ac.id/free-contents/downloadpdf.php/buku/multivariate-observations-g-a-f-seber-11683.pdf

Multivariate observations / G.A.F. Seber

Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora of real-life applications. However, they suffer from the important shortcomings of their high time and memory training complexities, which depend on the training set size. This issue is especially challenging nowadays, since the amount of data generated every second becomes tremendously large in many domains. This review provides an extensive survey on existing methods for selecting SVM training data from large datasets. We divide the state-of-the-art techniques into several categories. They help understand the underlying ideas behind these algorithms, which may be useful in designing new methods to deal with this important problem. The review is complemented with the discussion on the future research pathways which can make SVMs easier to exploit in practice.

/pdf/selecting-training-sets-for-support-vector-machines-a-review-1z2uussw0t.pdf

Selecting training sets for support vector machines: a review

Deep neural networks (DNNs) have achieved unprecedented success in a wide array of tasks. However, the performance of these systems depends directly on their hyper-parameters which often must be selected by an expert. Optimizing the hyper-parameters remains a substantial obstacle in designing DNNs in practice. In this work, we propose to select them using particle swarm optimization (PSO). Such biologically-inspired approaches have not been extensively exploited for this task. We demonstrate that PSO efficiently explores the solution space, allowing DNNs of a minimal topology to obtain competitive classification performance over the MNIST dataset. We showed that very small DNNs optimized by PSO retrieve promising classification accuracy for CIFAR-10. Also, PSO improves the performance of existing architectures. Extensive experimental study, backed-up with the statistical tests, revealed that PSO is an effective technique for automating hyper-parameter selection and efficiently exploits computational resources.

Particle swarm optimization for hyper-parameter selection in deep neural networks

Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

/pdf/data-augmentation-for-brain-tumor-segmentation-a-review-37t7iastkt.pdf

Data Augmentation for Brain-Tumor Segmentation: A Review.

Spatial-based skin detection using discriminative skin-presence features

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images

/pdf/self-adaptive-algorithm-for-segmenting-skin-regions-4qi25nia37.pdf

Michal Kawulok

Papers

Selecting training sets for support vector machines: a review

Particle swarm optimization for hyper-parameter selection in deep neural networks

Data Augmentation for Brain-Tumor Segmentation: A Review.

Spatial-based skin detection using discriminative skin-presence features

Self-adaptive algorithm for segmenting skin regions