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Showing 1 to 16 of 16 for “"Feature Construction"”.
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Explanation-Based Feature Construction
… training data is limited. The crucial stage of feature construction, often done manually, plays a significant role in allowing such information to be incorporated into a learner. We propose algorithms for automated feature construction where available domain knowledge, even though imperfect and …
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Using Dependency Parses to Augment Feature Construction for Text Mining
… representations such as bag-of-words and similar feature-based models. With the advent of modern high performance computing, deep sentence level linguistic analysis of large scale text corpora has become practical. In this dissertation, we evaluate the utility of dependency parses as textual …
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Feature construction: An analytic framework and an application to decision trees
… success largely depends upon the quality of the features used to describe the examples. When a learning problem uses low-level features, the complexity of the concept-membership function can make SBL inaccurate, expensive, or simply impossible. One way to overcome this limitation is through …
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Model-based feature construction and text representation for social media analysis
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms
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Novel feature construction technique for detecting anomalous faces and evaluating style transfer methods
… careful design and evaluation of deep-learned features are still necessary like hand-crafted features for computer vision tasks. We demonstrate this in two different domain problems – Anomaly Detection and Style Transfer. We present feature aggregation techniques and also quantitative …
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Machine Learning for Radio Frequency Interference Flagging
… a two dimensional filtering technique for feature construction. The goal of this method is intended to implement a means of injecting a form of spatial information of nearby time/frequency samples for each sample in a spectrogram. The inclusion of this spacial information, which is relevant …
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Time Series Learning With Probabilistic Network Composites
… type of unsupervised learning in which the feature construction (or extraction) step is modified to account for multiple sources of data and to systematically search for embedded temporal patterns. This modified technique is combined with traditional cluster definition methods to provide an …
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Computational Fluid Dynamics with Embedded Cut Cells on Graphics Hardware
… for all closed surfaces. We discuss efficient feature construction and work scheduling and demonstrate high-speed distance generation for complex geometries. At the core of our simulation implementation is a split Euler solver for high-speed flow. We present a one-dimensional method that …
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Modelling prognostic trajectories in Alzheimer’s disease
… tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread …
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Loss pattern recognition and profitability prediction for insurers through machine learning
… discuss the preprocessing techniques used for feature construction. In Chapter 4, we propose a new model with the objective to develop a new risk index which represents clients' potential future risk level. We then compare the performance of our new index with the original risk index used by …
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Automated Bandgap Correction in Double Perovskites: An Active Learning Framework Using Hybrid Functional Density Functional Theory and Delta Machine Learning
… labels. A unique aspect of this research is the construction and utilization of a unified oxide double-perovskite candidate universe that does not rely upon chemically convenient preselection. The explored space was built by combining reported Materials Project A2BB′O6 structures with Glazer …
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Unsupervised feature analysis for high dimensional big data
… which could work well even without supervision. Feature analysis has been proven effective and important for many applications. Feature analysis is a broad research field, whose research topics includes but are not limited to feature selection, feature extraction, feature construction, and …
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Combinación de clasificadores: construcción de características e incremento de la diversidad
… nominales", "Disturbing Neighbors" y "Random Feature Weights". Las Cascadas permiten que clasificadores que necesitan entradas numéricas mejoren sus resultados, tomando como entradas adicionales las estimaciones de probabilidad de otro clasificador que sí pueda trabajar con datos nominales. …
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Use of prior knowledge in classification of similar and structured objects
Statistical machine learning has achieved great success in many fields in the last few decades. However, there remain classification problems that computers still struggle to match human performance. Many such problems share the same properties---large within class variability and complex structure …
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Αναγνώριση επιθέσεων άρνησης εξυπηρέτησης
Στη Διδακτορική Διατριβή μελετώνται 3 κατηγορίες επιθέσεων άρνησης εξυπηρέτησης (Denial-of-Service). Η πρώτη κατηγορία αφορά επιθέσεις τύπου SYN Flood, μια επίθεση που πραγματοποιείται σε χαμηλό επίπεδο και αποτελεί την πιο διαδεδομένη ίσως κατηγορία. Για την αναγνώριση των επιθέσεων αυτών …