172,240 research outputs found
A Comparison of Multi-instance Learning Algorithms
Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms.
This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems
Combining Labelled and Unlabelled Data in the Design of Pattern Classification Systems
There has been much interest in applying techniques that incorporate knowledge from unlabelled data
into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on
real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data.
In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised
approaches using different ratios of labelled to unlabelled samples is presented. The experimental results
show that when supported by unlabelled samples much less labelled data is generally required to build a classifier
without compromising the classification performance. If only a very limited amount of labelled data is available the
results show high variability and the performance of the final classifier is more dependant on how reliable the labelled
data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and
unlabelled data have been shown to offer most significant improvements when natural clusters are present in the
considered problem
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
In a real world, pattern recognition problems in diversified forms are ubiquitous and are critical in most human decision making tasks. In pattern recognition system, achieving high accuracy in pattern classification is crucial. There are two general paradigms for pattern recognition classification which are supervised and unsupervised learning. The problems in applying unsupervised learning/clustering is that this method requires teacher during the classification process and it has to learn independently which may lead to poor classification. Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. In addition, the labelling is time consuming and done manually. To solve the problems mentioned, integration of unsupervised clustering algorithm and the supervised classifier is proposed. The objective of this research is to study the performance/capability of the integration between both unsupervised and supervised learning.
In order to achieve the objective, this research is separated into two phases. Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. By adopting the proposed integration system, the limitation of the unsupervised clustering method can be overcome and for supervised learning, the labelling time can be reduced and more training examples are labelled which can be used to train for supervised classifier. As the result, the pattern classification accuracy is also
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increase. For examples, after applying the proposed integration system, the classification accuracy of Fisher’s Iris, Wine and Bacteria18Class has been increased from 88.67% to 96.00%, from 78.33% to 83.45% and from 93.33% to 94.67% respectively as compared to only used unsupervised clustering algorithm. The result has shown that the proposed integration system could be applied to increase the performance of the classification. However, further study is needed in the feature extraction and clustering algorithms part as the performance of the pattern classification is still depending on the data input
Unsupervised Single Image Deraining with Self-supervised Constraints
Most existing single image deraining methods require learning supervised
models from a large set of paired synthetic training data, which limits their
generality, scalability and practicality in real-world multimedia applications.
Besides, due to lack of labeled-supervised constraints, directly applying
existing unsupervised frameworks to the image deraining task will suffer from
low-quality recovery. Therefore, we propose an Unsupervised Deraining
Generative Adversarial Network (UD-GAN) to tackle above problems by introducing
self-supervised constraints from the intrinsic statistics of unpaired rainy and
clean images. Specifically, we firstly design two collaboratively optimized
modules, namely Rain Guidance Module (RGM) and Background Guidance Module
(BGM), to take full advantage of rainy image characteristics: The RGM is
designed to discriminate real rainy images from fake rainy images which are
created based on outputs of the generator with BGM. Simultaneously, the BGM
exploits a hierarchical Gaussian-Blur gradient error to ensure background
consistency between rainy input and de-rained output. Secondly, a novel
luminance-adjusting adversarial loss is integrated into the clean image
discriminator considering the built-in luminance difference between real clean
images and derained images. Comprehensive experiment results on various
benchmarking datasets and different training settings show that UD-GAN
outperforms existing image deraining methods in both quantitative and
qualitative comparisons.Comment: 10 pages, 8 figure
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End-to-End Machine Learning Frameworks for Medicine: Data Imputation, Model Interpretation and Synthetic Data Generation
Tremendous successes in machine learning have been achieved in a variety of applications such as image classification and language translation via supervised learning frameworks. Recently, with the rapid increase of electronic health records (EHR), machine learning researchers got immense opportunities to adopt the successful supervised learning frameworks to diverse clinical applications. To properly employ machine learning frameworks for medicine, we need to handle the special properties of the EHR and clinical applications: (1) extensive missing data, (2) model interpretation, (3) privacy of the data. This dissertation addresses those specialties to construct end-to-end machine learning frameworks for clinical decision support. We focus on the following three problems: (1) how to deal with incomplete data (data imputation), (2) how to explain the decisions of the trained model (model interpretation), (3) how to generate synthetic data for better sharing private clinical data (synthetic data generation). To appropriately handle those problems, we propose novel machine learning algorithms for both static and longitudinal settings. For data imputation, we propose modified Generative Adversarial Networks and Recurrent Neural Networks to accurately impute the missing values and return the complete data for applying state-of-the-art supervised learning models. For model interpretation, we utilize the actor-critic framework to estimate feature importance of the trained model's decision in an instance level. We expand this algorithm to active sensing framework that recommends which observations should we measure and when. For synthetic data generation, we extend well-known Generative Adversarial Network frameworks from static setting to longitudinal setting, and propose a novel differentially private synthetic data generation framework.To demonstrate the utilities of the proposed models, we evaluate those models on various real-world medical datasets including cohorts in the intensive care units, wards, and primary care hospitals. We show that the proposed algorithms consistently outperform state-of-the-art for handling missing data, understanding the trained model, and generating private synthetic data that are critical for building end-to-end machine learning frameworks for medicine
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation
Appearance changes due to weather and seasonal conditions represent a strong
impediment to the robust implementation of machine learning systems in outdoor
robotics. While supervised learning optimises a model for the training domain,
it will deliver degraded performance in application domains that underlie
distributional shifts caused by these changes. Traditionally, this problem has
been addressed via the collection of labelled data in multiple domains or by
imposing priors on the type of shift between both domains. We frame the problem
in the context of unsupervised domain adaptation and develop a framework for
applying adversarial techniques to adapt popular, state-of-the-art network
architectures with the additional objective to align features across domains.
Moreover, as adversarial training is notoriously unstable, we first perform an
extensive ablation study, adapting many techniques known to stabilise
generative adversarial networks, and evaluate on a surrogate classification
task with the same appearance change. The distilled insights are applied to the
problem of free-space segmentation for motion planning in autonomous driving.Comment: In Proceedings of the 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017
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