Unified backpropagation for multi-objective learning
Learning enhancement of three-term backpropagation network based on elitist multi-objective evolutionary algorithms
... Irani et al., 2011; Tang et al., 2011; Wang et al., 2011; Wang and Qian; Yi et al., 2014; Yu and Peng, 2012). They have proved that these kinds of algorithm are feasible and effective for this task. This is because the ...
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Multi objective genetic algorithm for training three term backpropagation network
... ABSTRACT. Multi Objective Evolutionary Algorithms has been applied for learning problem in Artificial Neural Networks to improve the generalization of the training and testing unseen ...(TTBPN) ...
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Multi-Objective Supervised Learning
... of multi-objective supervised learning that are worth highlighting: Hybrid models: Usually researchers tend to either start a process with a ‘traditional’ local optimiser (like gradient descent in ...
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Multi-Objective Supervised Learning
... supervised learning? Supervised learning is the term applied in the machine learning field to tech- niques for inducing a function mapping between pairs of inputs and desired outputs – based on some ...
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Solving Multi-Objective Optimization Problems through Unified Approach
... paper, unified approach for solving multi- objective optimization problem is ...the unified approach are constructed through the ARP and the weak efficient ...Keywords: Multi- ...
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Multi-Objective ROC learning for classification
... In this thesis a multi-objective evolutionary algorithm (MOEA) is used to find clas- sifiers whose ROC graph locations are Pareto optimal. The Relevance Vector Machine (RVM) is a state-of-the-art classifier ...
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Special issue on multi-objective reinforcement learning
... el multi-objective RL algorithm is the Linked Rings problem ...three objective environments with stochastic transitions which are related to strategic ...above multi-objective ...
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ACCELERATING MACHINE LEARNING VIA MULTI-OBJECTIVE OPTIMIZATION
... Summary In order to optimize for performance and accuracy, a clear understanding of the optimization landscape is needed. This dissertation work outlines where the potentional opportunites are for optimizing performance ...
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Multi-objective Path Finding Using Reinforcement Learning
... one objective that the software program is trying to achieve such as, minimizing the overall distance traveled or reducing the time taken to travel from one point to the ...single objective, the problem at ...
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Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
... Abstract — The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we ...
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A Unified Multi task Adversarial Learning Framework for Pharmacovigilance Mining
... inspired multi- task learning framework that can simultane- ously extract ADRs from various ...adversarial learning-based ap- proach to learn features across multiple ADR information ...
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Multi-objective reinforcement learning for AUV thruster failure recovery
... single- objective optimization approach was used and we employed a scalarized objective function, even though the defined objec- tives were ...a multi-objective algorithm is employed that is ...
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Fuzzy Criteria in Multi-objective Feature Selection for Unsupervised Learning
... unsupervised learning schemas in order to produce several candidate solutions for ...unsupervised-based multi-objective heuristic optimization algorithm is becoming an attractive approach, that has ...
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Multi-objective reinforcement learning with continuous pareto frontier approximation
... Following such approach, two improvements can be eas- ily obtained. First, the number of free parameters decreases and, as a consequence, the learning process simplifies. Sec- ond, the approximate frontier is ...
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Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach
... attempted multi-objective RL for learning takeover maneuver [ 35 ], where they scalarized the learned Q functions of each objective by weighted sum to form a single ...3. ...
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An investigation of multi-objective hyper-heuristics for multi-objective optimisation
... ranking scheme that relies on choosing a heuristic with the best SSC value at the right time (decision point) to guide the search to move toward more spaces around the POF. This result is more reliable as shown in Figure ...
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A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning
... for Multi-Objective Multi-Agent Reinforcement Learning Kristof Van Moffaert, Tim Brys, Arjun Chandra, Lukas Esterle, Peter ...solve multi-objective problems, multiple re- ward ...
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A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty
... a multi- objective optimisation problem, where trade-offs between the con- flicting objectives, ...with multi-objective optimisa- tion and uncertainty simultaneously, usually employ an ...
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Fuzzy multi objective optimization: With reference to multi objective transportation problem
... solve multi objectives cutting parameter optimization in the presence of fuzzy ...of multi-objective multi-persons decision making by using fuzzy ...propagation learning algorithm under ...
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Using the XCS classifier system for multi objective reinforcement learning problems
... 0.5. A cost of 0.01 energy points is deducted for each move made by the animat in the grid world, in the same way that a physical robot’s movement has an energetic cost. The animat’s energy level is not allowed to go ...
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