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- Concept drift (2)
- Deep learning (2)
- Incremental learning (2)
- Machine learning (2)
- Affine invariance (1)
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- Alignment (1)
- Artificial neural network (1)
- CNN (1)
- Causality (1)
- Classification (1)
- Clustering (1)
- Computer science (1)
- Conflation (1)
- Convolutional Neural Network (1)
- Correlation (1)
- Data Mining (1)
- Dimension reduction (1)
- Face detection (1)
- Face matching (1)
- Feature extraction (1)
- GMTI (1)
- General Line Coordinates (1)
- Genetic Algorithm (1)
- Guidance (1)
- Image (1)
- Image processing (1)
- Information bottleneck (1)
- Interactive visualization (1)
- Knowledge discovery (1)
- Linear relations (1)
Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Weighted Incremental–Decremental Support Vector Machines For Concept Drift With Shifting Window, Honorius Gâlmeanu, Răzvan Andonie
Weighted Incremental–Decremental Support Vector Machines For Concept Drift With Shifting Window, Honorius Gâlmeanu, Răzvan Andonie
Computer Science Faculty Scholarship
We study the problem of learning the data samples’ distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Machines (SVMs) can learn weighted input instances and that they can also be trained online (incremental–decremental learning). Combining these two SVM properties, the open problem is to define an online SVM concept drift model with shifting weighted window. The classic SVM model should be retrained from scratch after each window shift. We introduce the Weighted Incremental–Decremental SVM (WIDSVM), …
Information Bottleneck In Deep Learning - A Semiotic Approach, Bogdan Musat, Razvan Andonie
Information Bottleneck In Deep Learning - A Semiotic Approach, Bogdan Musat, Razvan Andonie
Computer Science Faculty Scholarship
The information bottleneck principle was recently proposed as a theory meant to explain some of the training dynamics of deep neural architectures. Via information plane analysis, patterns start to emerge in this framework, where two phases can be distinguished: fitting and compression. We take a step further and study the behaviour of the spatial entropy characterizing the layers of convolutional neural networks (CNNs), in relation to the information bottleneck theory. We observe pattern formations which resemble the information bottleneck fitting and compression phases. From the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency …
Concept Drift Adaptation With Incremental–Decremental Svm, Honorius Gâlmeanu, Răzvan Andonie
Concept Drift Adaptation With Incremental–Decremental Svm, Honorius Gâlmeanu, Răzvan Andonie
Computer Science Faculty Scholarship
Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a …
Learning In Convolutional Neural Networks Accelerated By Transfer Entropy, Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Learning In Convolutional Neural Networks Accelerated By Transfer Entropy, Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Computer Science Faculty Scholarship
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. …
Deep Learning Of 2-D Images Representing N-D Data In General Line Coordinates, Dmytro Dovhalets, Boris Kovalerchuk, Szilárd Vajda, Răzvan Andonie
Deep Learning Of 2-D Images Representing N-D Data In General Line Coordinates, Dmytro Dovhalets, Boris Kovalerchuk, Szilárd Vajda, Răzvan Andonie
Computer Science Faculty Scholarship
While knowledge discovery and n-D data visualization procedures are often efficient, the loss of information, occlusion, and clutter continue to be a challenge. General Line Coordinates (GLC) is a rather new technique to deal with such artifacts. GLC-Linear, which is one of the methods in GLC, allows transforming n-D numerical data to their visual representation as polylines losslessly. The method proposed in this paper uses these 2-D visual representations as input to Convolutional Neural Network (CNN) classifiers. The obtained classification accuracies are close to the ones obtained by other machine learning algorithms. The main benefit of the method is the …
Looking At Faces In The Wild, Eugene Borovikov, Szilárd Vajda, Michael Bonifant, Michael Gill
Looking At Faces In The Wild, Eugene Borovikov, Szilárd Vajda, Michael Bonifant, Michael Gill
Computer Science Faculty Scholarship
Recent advances in the face detection (FD) and recognition (FR) technology may give an impression that the problem of face matching is essentially solved, e.g. via deep learning models using thousands of samples per face for training and validation on the available benchmark data-sets. Human vision system seems to handle face localization and matching problem differently from the modern FR systems, since humans detect faces instantly even in most cluttered environments, and often require a single view of a face to reliably distinguish it from all others. This prompted us to take a biologically inspired look at building a cognitive …
Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data, Boris Kovalerchuk, Dmytro Dovhalets
Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data, Boris Kovalerchuk, Dmytro Dovhalets
Computer Science Faculty Scholarship
The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent …
Correlation Of Partial Frames In Video Matching, Boris Kovalerchuk, Sergei Kovalerchuk
Correlation Of Partial Frames In Video Matching, Boris Kovalerchuk, Sergei Kovalerchuk
Computer Science Faculty Scholarship
Correlating and fusing video frames from distributed and moving sensors is important area of video matching. It is especially difficult for frames with objects at long distances that are visible as single pixels where the algorithms cannot exploit the structure of each object. The proposed algorithm correlates partial frames with such small objects using the algebraic structural approach that exploits structural relations between objects including ratios of areas. The algorithm is fully affine invariant, which includes any rotation, shift, and scaling.
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Computer Science Faculty Scholarship
Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.
Visual Discovery In Multivariate Binary Data, Boris Kovalerchuk, Florian Delizy, Logan Riggs, Evgenii Vityaev
Visual Discovery In Multivariate Binary Data, Boris Kovalerchuk, Florian Delizy, Logan Riggs, Evgenii Vityaev
Computer Science Faculty Scholarship
This paper presents the concept of Monotone Boolean Function Visual Analytics (MBFVA) and its application to the medical domain. The medical application is concerned with discovering breast cancer diagnostic rules (i) interactively with a radiologist, (ii) analytically with data mining algorithms, and (iii) visually. The coordinated visualization of these rules opens an opportunity to coordinate the rules, and to come up with rules that are meaningful for the expert in the field, and are confirmed with the database. This paper shows how to represent and visualize binary multivariate data in 2-D and 3-D. This representation preserves the structural relations that …
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Computer Science Faculty Scholarship
The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and …