Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 50 for “"Incremental learning"”.
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Incremental Learning in Regression Contexts
Incremental or Online Learning is a Machine Learning paradigm in which models are updated with each new incoming data sample. In the real world, these models are usually deployed on Data Streams, that are potentially infinite in size and therefore cannot be tackled by common Batch or Offline …
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Interactive and Incremental Learning via a Multisensory Mobile Robot
… these analyses and discussions, we develop our learning scheme that can learn online from both labeled and unlabeled data. The learning scheme is based on successive refinement of the decision boundary.
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Incremental learning for large-scale stream data and its application to cybersecurity
… As many up-to-date data are used during the learning, the detection system would become more robust to the latest strategy of the cybercriminal. Therefore, the scenario mentioned above can be avoided by assisting the user with important information at the user-end such as malicious spam email …
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Boosted ensemble algorithm strategically trained for the incremental learning of unbalanced data
… problems require a solution that needs to be incrementally updated over a period of time. Incremental learning problems are often complicated by the appearance of new concept classes and unbalanced cardinality in training data. The purpose of this research is to develop an algorithm capable of …
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Referencing Unlabelled World Data to Prevent Catastrophic Forgetting in Class-incremental Learning
… of "catastrophic forgetting" in deep continual-learning systems. The term refers to severe performance degradation for older tasks, as a system learns new tasks that are presented sequentially. Most previous techniques have emphasized preservation of existing knowledge while learning new tasks, …
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Incremental learning algorithm for anomaly detection applied to computed tomography scans in nuclear industry
… parts) or not (defect-free). Furthermore, the incremental learning (IL) concept has been introduced for supervised anomaly detection and is suitable for data-restricted applications. Existing IL approaches have employed ML techniques such as Naive Bayes or proximity measures such as nearest …
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Assessing classification confidence using a weighted exponential based technique with the Learn++ incremental learning algorithm
… own reliability. In this work, the supervised incremental learning algorithm Learn++ [1, 2], which exploits the synergistic power of an ensemble of classifiers, is further developed to add the capability of assessing its own confidence. Estimation of the true generalization performance of the …
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An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning
The principal dilemma in a learning process, whether human or computer, is adapting to new information, especially in cases where this new information conflicts with what was previously learned. The design of computer models for incremental learning is an emerging topic for classification and …
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Real-Time Machine Learning for Quickest Detection
… Systems (CPS) require real-time machine learning for control and decision making. One promising solution is to use deep learning to discover useful patterns for event detection from heterogeneous data. However, deep learning algorithms encounter challenges in CPS with assurability …
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Incremental lexical learning in speech production: a computational model and empirical evaluation
… in speech production that applies error-driven learning to its lexical activation network. This model naturally produces repetition priming and semantic interference effects. It predicts the major findings from several published experiments, demonstrating that these effects may arise from …
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Multi-Task Reinforcement Learning: From Single-Agent to Multi-Agent Systems
… as well as deficiencies of multi-task learning due to a phenomenon known as catastrophic forgetting. In this thesis, we present a set of simulation environments for exploring the abilities of multi-task drone systems and provide a platform for testing agents in incremental single-agent …
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Temporal Data Mining in a Dynamic Feature Space
… address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.
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Novel machine learning approaches for modeling variations in semiconductor manufacturing
… primary goal of this thesis is to build machine learning models for variation analysis and yield improvement. In this thesis, we first propose a novel method to estimate and characterize spatial variations on dies or wafers. This new technique exploits recent developments in matrix completion, …
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Robust electronic circuit design using evolutionary and Taguchi methods
… used in this thesis namely, Population Based Incremental Learning ( PBIL ) and Cross generational selection Heterogeneous crossover Cataclysmic mutation ( CHC ), are tested on a series of circuit problems to fmd if robust electronic circuits can be built from evolutionary methods. The …
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Continual Learning of Object Classification in the Real World
Technological advances in deep learning have brought remarkable performance in the object classification task but only when all the training data of classes to be learned are available at the same time. However, real-world data continually evolve through time, resulting in ever-changing learning …
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DL-DI: A Deep Learning Framework for Distributed, Incremental Image Classification
Deep Learning technologies show promise for dramatic advances in fields such as image classification and speech recognition. Deep Learning (DL) is a class of Machine Learning algorithms that involves learning of multiple levels of features from data to build a model. One of the open questions in DL …
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SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
… from big data. However, traditional machine learning approaches are not well fit to analyze the full value of big data. Explicitly, current research and practice of Machine learning do not fully support some important features for big data analytics such as incremental learning, distributed …
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Application of differential evolution to power system stabilizer design
… heuristic algorithms such as Population Based Incremental Learning (PBIL) and Differential Evolution (DE), etc., have received increasing attention.
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Distributed On-line Training for Object Detection on Embedded Devices.
… etc for embedded devices. Use of Transfer Learning technique, Data Parallelism and Model Parallelism in this study reduces the resource consumption of such devices. Besides, retraining the model with continuous streaming data helps to improve the accuracy of the model as well as to further …
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Polymorphic Adversarial DDoS attack on IDS using GAN
… of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks.
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