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When large scale disaster occurs suddenly, allocating scheme of the first batch of "life" emergency supplies directly determines rescue effect. To make lack loss evaluation of the supplies accordant with actual cases, this paper proposed nonlinear loss evaluation function, based on which constructed multi to multi constrained scheduling model with multi objectives. To improve the performance...
There exist optimization problems with the target objective, which is to be optimized, and several extra objectives, which can be helpful in the optimization process. The EA+RL method is designed to control optimization algorithms which solve problems with extra objectives. The method is based on the use of reinforcement learning for adaptive online selection of objectives.
The goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. A learning algorithm is stable if the algorithm satisfies the hypothesis that the output of the algorithm varies in a limited way in response to small changes made to the training set. This paper studies the `almost everywhere' stability of ranking algorithms, notions of strong...
Distributed Hierarchical Graph Neuron (DHGN) is a single-cycle learning distributed pattern recognition algorithm, which reduces the computational complexity of existing pattern recognition algorithms by distributing the recognition process into smaller clusters. This paper investigates an effect of dividing and distributing simple pattern recognition processes within a computational network. Our...
In this paper, a new multi-objective reinforcement learning algorithm for multi-objective sequential decision making problems in unknown environment is proposed. The salient characters of the algorithm are: (1) decision maker's objective preference is introduced to guide learning direction; (2) a new measure of comparing action decisions under several objectives based on the fuzzy inference system...
This paper combines artificial immune and emotional learning methods to solve the path optimization problems in complex, dynamic and real-time multi-agent systems. In artificial immune algorithm, path metric is defined as the affinity function between antigen and antibody, namely, the matching degree between optimal path and candidate paths. At the same time, emotional learning method is used to train...
Due to the raising complexity in distributed embedded systems, a single designer will not be able to plan and organize the communication for such systems. Therefore, it will get more and more important to relieve the designer in that task. Our idea is a communication system that is capable to organize itself to satisfy predefined properties. In this paper, we want to solve the problem of establishing...
The success of transfer to improve learning on a target task is highly dependent on the selected source data. Instance-based transfer methods reuse data from the source tasks to augment the training data for the target task. If poorly chosen, this source data may inhibit learning, resulting in negative transfer. The current best performing algorithm for instance-based transfer, TrAdaBoost, performs...
Image search has recently gained more and more attention for various applications. To capture users' intensions and to bridge the gap between the low level visual features and the high level semantics, a dozen of interactive reranking (IR) or relevance feedback (RF) algorithms have been developed and achieved significant performance improvements. In this paper, we develop a novel subspace learning...
High pressure dissolving (HPD) is a very important process for alumina production. During HPD process, alumina caustic ratio (ACR) of the dissolved slurry is a very important economic technical indicator. In practice, there are many factors influencing ACR and there are different noise levels for different HPD conditions. So, it is very difficult to predict ACR with single model accurately. In this...
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