136,919 research outputs found
Feature Selection based on Mutual Information
The application of machine learning models such as
support vector machine (SVM) and artificial neural networks
(ANN) in predicting reservoir properties has been effective in the
recent years when compared with the traditional empirical
methods. Despite that the machine learning models suffer a lot in
the faces of uncertain data which is common characteristics of
well log dataset. The reason for uncertainty in well log dataset
includes a missing scale, data interpretation and measurement
error problems. Feature Selection aimed at selecting feature
subset that is relevant to the predicting property. In this paper a
feature selection based on mutual information criterion is
proposed, the strong point of this method relies on the choice of
threshold based on statistically sound criterion for the typical
greedy feedforward method of feature selection. Experimental
results indicate that the proposed method is capable of improving
the performance of the machine learning models in terms of
prediction accuracy and reduction in training time
The uncertain representation ranking framework for concept-based video retrieval
Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
In order to facilitate long-term localization using a visual simultaneous
localization and mapping (SLAM) algorithm, careful feature selection can help
ensure that reference points persist over long durations and the runtime and
storage complexity of the algorithm remain consistent. We present SIVO
(Semantically Informed Visual Odometry and Mapping), a novel
information-theoretic feature selection method for visual SLAM which
incorporates semantic segmentation and neural network uncertainty into the
feature selection pipeline. Our algorithm selects points which provide the
highest reduction in Shannon entropy between the entropy of the current state
and the joint entropy of the state, given the addition of the new feature with
the classification entropy of the feature from a Bayesian neural network. Each
selected feature significantly reduces the uncertainty of the vehicle state and
has been detected to be a static object (building, traffic sign, etc.)
repeatedly with a high confidence. This selection strategy generates a sparse
map which can facilitate long-term localization. The KITTI odometry dataset is
used to evaluate our method, and we also compare our results against ORB_SLAM2.
Overall, SIVO performs comparably to the baseline method while reducing the map
size by almost 70%.Comment: Published in: 2019 16th Conference on Computer and Robot Vision (CRV
Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images
We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when\ud
more features are selected
Measuring Information Leakage in Website Fingerprinting Attacks and Defenses
Tor provides low-latency anonymous and uncensored network access against a
local or network adversary. Due to the design choice to minimize traffic
overhead (and increase the pool of potential users) Tor allows some information
about the client's connections to leak. Attacks using (features extracted from)
this information to infer the website a user visits are called Website
Fingerprinting (WF) attacks. We develop a methodology and tools to measure the
amount of leaked information about a website. We apply this tool to a
comprehensive set of features extracted from a large set of websites and WF
defense mechanisms, allowing us to make more fine-grained observations about WF
attacks and defenses.Comment: In Proceedings of the 2018 ACM SIGSAC Conference on Computer and
Communications Security (CCS '18
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