Abstract: Data matter. How can we quantitatively and objectively evaluate data? Pricing data, or information goods, has been studied and practiced in many disciplines, including economics, marketing, electronic commerce, data management, data mining, and machine…
Abstract: Pediatric neuroblastoma is deadly. Treatment plans depend on neuroblastoma survival prediction. We proposed a heterogeneous ensemble learning method to predict neuroblastoma patient survival and extracted decision rules to help doctors make decisions. Five heterogeneous…
Abstract: Active learning (AL) queries labels from unlabeled data to improve classification hypothesis generalization. Informative, representative, or diverse evaluation policies evaluate sampling. In a cold-start hypothesis, the policy, which requires an initial labeled set, may…
Abstract: Machine learning and data processing use nonconvex regularized optimization problems. The consensus optimization used the iteratively reweighted algorithm due to problem structure. This paper proposes adding an inertial term to each iteration to accelerate…
Abstract: Scene parsing, robot motion planning, and autonomous driving require accurate scene classification. Deep recognition models have performed well for a decade. However, these deep architectures cannot encode human visual perception, including gaze movements and…
Abstract: Deep neural networks (DNNs) solve complex machine learning issues. Function representation’s expressiveness helped them succeed. Since piecewise linear neural networks (PLNNs) use linear pieces to model complex patterns, the number of linear regions is…
Abstract: We design a platform for prototyping low-cost analog neural networks for on-chip integration with analog/RF circuits. Integration supports self-test, self-tuning, and trust/aging monitoring by classifying analog measurements from on-chip sensors. 1) Low energy and…
Abstract: Semi-supervised learning is one of the most appealing machine learning problems. This paper introduces Semi-supervised Adaptive Local Embedding learning (SALE), a locality-preserved dimensionality reduction framework that learns a local discriminative embedding by constructing a…
Abstract: Text mining, visual classification, and recommender systems use machine learning to make accurate predictions from data. When processing large datasets, most sophisticated machine learning methods take a long time. Large-scale machine learning (LML) is…
Abstract: Data similarity (or distance) computation is a fundamental research topic that underpins many high-level machine learning and data mining applications based on similarity measures. Big data’s “3V” nature—volume, velocity, and variety—makes exact similarity computation…
Abstract: Hybrid systems, which combine statistical and machine learning (ML) techniques using residual (error forecasting) modeling, have been highlighted in the literature for their accuracy and ability to forecast time series with different characteristics. Due…
Abstract: The explosion of modeling complex systems using attributed networks boosts anomaly detection research in such networks, which can be applied in many high-impact domains. Many existing attempts simply concatenate multiple views into a feature…
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