Unsupervised metric learning consists in building adaptive distance functions without knowledge of the class labels to improve pattern classification. Usually, this process can be accomplished by manifold learning alg...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Unsupervised metric learning consists in building adaptive distance functions without knowledge of the class labels to improve pattern classification. Usually, this process can be accomplished by manifold learning algorithms, through nonlinear dimensionality reduction. In this paper, we propose a curvature based Isometric Feature Mapping, a method that uses differential geometric concepts to build an intrinsic distance function that measures the variations of the local tangent spaces along shortest paths in the KNN graph, motivated by the Frenet-Serret equations and the notion of curvature. Experimental results with several real world datasets show that the proposed method is capable of producing classification performance comparable to other state-of-the-art dimensionality reduction based metric learning techniques, such as t-SNE and UMAP.
Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent year. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE ...
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Artificial immune algorithm is a new bionic algorithms, it becomes a hot spot. Artificial immune algorithm has self-adjustment ability and adaptive capacity of the environment and can deal with complex optimization pr...
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The proceedings contain 76 papers. The topics discussed include: template generation and selection algorithms;optimized datapath design by evolutionary computation;a performance evaluation method for optimizing embedd...
ISBN:
(纸本)076951944X
The proceedings contain 76 papers. The topics discussed include: template generation and selection algorithms;optimized datapath design by evolutionary computation;a performance evaluation method for optimizing embedded applications;a robust handshake for asynchronous system;detailed placement with net length constraints;Steiner tree construction based on congestion for the global routing problem;incorporating pattern prediction technique for energy efficient filter cache design;a survey of dynamic power optimization techniques;digital realization of analogue computing elements using bit streams;a mixed-mode delay-locked loop for wide-range operation and multiphase clock generation;dynamic hardware-software partitioning on reconfigurable system-on-chip;and catalog of hardware acceleration techniques for real-time reconfigurable system on chip.
During Legal information systems migrations, one major problem is to handle attribute value cleaning. In the paper, we first show many data cleaning steps process on the values of the same data attributes and their de...
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ISBN:
(纸本)3540285660
During Legal information systems migrations, one major problem is to handle attribute value cleaning. In the paper, we first show many data cleaning steps process on the values of the same data attributes and their derivations, and users may ignore or be puzzled by the data transforms that are defined to clean and transform the data sets, and such process can also be prone to error during process optimization. In this paper, we first define two major such problems as assignment conflict and range conflict,and giving problem definitions for such conflicts. Then we present two separate algorithms respectively to discover and solve the conflicts.
The proceedings contain 35 papers. The special focus in this conference is on Engineering applications. The topics include: A model for knowledge management in software industry;a metaprocesses-oriented methodology ba...
ISBN:
(纸本)9783319508795
The proceedings contain 35 papers. The special focus in this conference is on Engineering applications. The topics include: A model for knowledge management in software industry;a metaprocesses-oriented methodology based on RAS;user experiences in virtual reality environments navigation based on simple knowledge organization systems;induction of rules based on similarity relations for imbalance datasets;price direction prediction on high frequency data using deep belief networks;search techniques for automated proposal of data mining schemes;comparison between neuronal networks and ANFIS for wind speed-energy forecasting;improving the performance of leaves identification by features selection with genetic algorithms;impact of weight initialization on multilayer perceptron using fuzzy similarity quality measure;a genetic optimized cascade multilevel converter for power analysis;proposal for a hybrid expert system and an optimization model for the routing problem in the courier services;use of simulation in a service desk of an oilfield services company;effects of using multimodal transport over the logistics performance of the food chain of uchuva;a simulation model to improve customer service in an information security company;multi-agent approach for solving the dynamic home health care routing problem;a dynamic model of logistic management for obtaining activated carbon;a knowledge-based expert system for scheduling in services systems;simulation of the coffee berry borer expansion in Colombian crops using a model of multiple swarms and a note about sensitivity analysis for the soft constraints model.
The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of...
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The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer. (C) 2010 Elsevier Ltd. All rights reserved.
The article proposes information technology for solving the discrete optimization problems in Geographic Information Systems (GIS). The information model of geographic data (geodata) was further developed, which consi...
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ISBN:
(纸本)9781665426053
The article proposes information technology for solving the discrete optimization problems in Geographic Information Systems (GIS). The information model of geographic data (geodata) was further developed, which consists of a formalized combination of their spatial and attributive components. It takes into account relational, semantic and frame models for representing the knowledge of the attributive component in order to unify the branch geodata. The conceptual model of decision support in sectoral GIS has been further developed, which takes into account the information model of geodata and the dynamics of their change, the hierarchy of tasks of sectoral GIS, the input operational data, the function of preferences and the criterion of decision-making. A new method of decision support for GIS construction was developed on the base of the proposed conceptual model. The method of selection and determination of free parameters of swarm intelligence algorithms is developed to increase the effectiveness of GIS. It is suggested to determine the free parameters for individual swarm algorithms based on machine learning with reinforcement, namely the Q-Learning method. Based on this method Markov chains for the swarm algorithms were constructed. Reinforcement consisted in the expert analysis of the results obtained by a certain swarm algorithm. On the example of territorial administration, optimal values of the parameters for individual swarm algorithms were found.
Practical applications of scheduling, routing and other generic constrained optimization problems often involve an uncertainty in the values of the data presented in the problem data instances. On the contrary, most o...
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Multi-objective evolutionary algorithms have been shown to solve multi-objective optimization problems well and have been very widely used, but there are still drawbacks such as failure to develop sufficient environme...
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