C. L. Philip Chen
10 papers in the PaperMetrix corpus
Papers by this author
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A Euclidean metric based voice feature extraction method using IDCT cepstrum coefficient
2019
In this paper, we propose a new method for voice feature extraction by using a hierarchical clustering approach of inverse discrete cosine transform (IDCT) cepstrum coefficient. Since the IDCT cepstrum coefficient is transformed into the …
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Adaptive Classifier Ensemble Method Based on Spatial Perception for High-Dimensional Data Classification
2019 · IEEE Transactions on Knowledge and Data Engineering
Classifying high-dimensional small-size data is challenging in the field of pattern recognition. Traditional ensemble learning methods have several limitations: 1) sample-space based methods are easily affected by noise and redundant features; 2) feature-space based methods …
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Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction
2020 · IEEE Transactions on Neural Networks and Learning Systems
How to make full use of the evolution information of chaotic systems for time-series prediction is a difficult issue in dynamical system modeling. In this article, we propose a maximum information exploitation broad learning system …
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Flexible Label-Induced Manifold Broad Learning System for Multiclass Recognition
2023 · IEEE Transactions on Neural Networks and Learning Systems
Broad learning system (BLS), which emerges as a lightweight network paradigm, has recently attracted great attention for recognition problems due to its good balance between efficiency and accuracy. However, the supervision mechanism in BLS and …
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Pruned Broad Learning System Based on Sparse Ridge Fusion
2023 · Research Square
<title>Abstract</title> Broad learning system is an emerging method, which has achieved outstanding performance in regression and classification problems. This paper proposes a novel algorithm called Pruned Broad Learning System (PBLS) to reduce model size and …
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SOP-GPT: A Framework for AI Agents Based on Artificial Intelligence-Generated Content
2024
The advancements in automated problem-solving were explored by the agents of Artificial Intelligence (AI)Generated Content (AIGC). Although existing AIGC-based AI Agent systems can solve simple tasks, it is complicated to handle complex tasks due to …
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A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
2024 · arXiv (Cornell University)
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction due to their excellent performance. However, new viewpoints …
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Ensemble Denoising Autoencoders Based on Broad Learning System for Time-Series Anomaly Detection
2025 · IEEE Transactions on Neural Networks and Learning Systems
Time-series anomaly detection has gained considerable prominence in numerous practical applications across various domains. Nonetheless, the scarcity of labels leads to the neglect of anomalous patterns in data, as well as the inherent complexities and …
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Machine Learning and Gaussian Mixture Model for Delineating Soil Cadmium Risk Zones
2025 · Ecosystem Health and Sustainability
Effective management of regional soil cadmium (Cd) contamination is limited by difficulties in accurately predicting Cd concentrations and delineating the risk zones. This study proposed an integrated approach, combining a Machine Learning Spatial Information Enhancement …
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LLM4Rail: An LLM-Augmented Railway Service Consulting Platform
2025 · arXiv (Cornell University)
Large language models (LLMs) have significantly reshaped different walks of business. To meet the increasing demands for individualized railway service, we develop LLM4Rail - a novel LLM-augmented railway service consulting platform. Empowered by LLM, LLM4Rail …