2 edition of **heuristic method of optimal generalized hypercube encoding for pictorial databases** found in the catalog.

heuristic method of optimal generalized hypercube encoding for pictorial databases

C. C. Yang

- 357 Want to read
- 7 Currently reading

Published
**1980** by Naval Research Laboratory in Washington, D.C .

Written in English

- Input design, Computer

**Edition Notes**

Statement | C.C. Yang, S.K. Chang and K.K. Singh |

Series | NRL report -- 8382 |

Contributions | Chang, S. K. 1944-, Singh, K. K., Naval Research Laboratory (U.S.). Space Systems Division. Systems Research Branch |

The Physical Object | |
---|---|

Pagination | iii, 31 p. ; |

Number of Pages | 31 |

ID Numbers | |

Open Library | OL14859536M |

Heuristic analysis in conversion rate optimization. is an approach to problem solving, learning or discovery that employs a practical method, not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Common examples Understanding Heuristic. To deal with the challenge of CRO, heuristic serves as an effective method. Presentations at conferences. Gruson, M., Cordeau, J.-F., Jans, R., “Benders Decomposition for a Two-Stage Three-Level Lot Sizing and Replenishment Problem. Heuristic based search space pruning Ensembles of local optimal solutions from COMPUTER DMF at Tsinghua University. hand, heuristic methods with time complexity bounded by a polynomial in the size parameters of the problem have been known for many decades. A comprehensive review of the multi-constrained knapsack problem and the associated heuristic algorithms is given by Chu and Beasley [4]. Some of the ideas are also applicable to non MDKPs.

An existing heuristic method for, say, quickly assigning The Book Heuristic and adaptive techniques are applied to a variety of telecommunications related Tables and list complete results of the three methods. The GAs find optimal solutions at a fraction of the computational cost of branch and bound for.

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Similarity retrieval from a large pictorial database can be much more efficient by encoding the original database into certain convenient formats.

Generalized hypercube (GH) encoding is one such technique. To optimize GH coding, a heuristic approach has been formulated.

A heuristic method of optimal generalized hypercube encoding for pictorial databases Author: C C Yang ; S K Chang ; K K Singh ; Naval Research Laboratory (U.S.). A HEURISTIC METHOD OF OPTIMAL GENERALIZED HYPERCUBE ENCODING FOR PICTORIAL DATABASES INTRODUCTION In similarity, retrieval from a pictorial database [ 1], pattern recognition [ 2,41, and clustering analysis, it is often desired to find the set of database records (or set of patterns.

Similarity retrieval from a large pictorial database can be much more efficient by encoding the original database into certain convenient formats. Generalized hypercube (GH) encoding is one such technique. To optimize GH coding, a heuristic approach has been formulated.

Two optimization problems have been considered here: First, given the handle length m, find the optimal GH sub m encoding. Latin hypercube designs (LHD) are widely used in the context of CSE.

The optimal LHD for a given dimension of problem is constructed by using search algorithms under pre-specified optimality criteria. This paper proposes the methods to enhance the performance of search algorithms which have been widely used in the context of by: 1. A Heuristic Method for Finding the Optimal Number of Clusters with Application in Medical Data.

(PDF Available) in Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society.

4/2/ 3 Admissible heuristics • A heuristic h(n) is admissible if for every node n, h(n) ≤h*(n), where h*(n) is the true cost to reach the goal state from n. • An admissible heuristic never overestimatesthe cost to reach the goal, i.e., it is optimisticFile Size: KB.

Even though the order of inputs to the ANN in (a) is correlated directly to the geometry of the front-facing robot sensors from which they are activated, traditional ANN learning algorithms such as backpropagation [45] or current neuroevolution methods [52, File Size: 2MB.

VaR and Expected Shortfall. In Computational Methods in Decision-making, Economics and Finance, (Eds. E.J. Konto-ghiorghes, B. Rustem and S. Siokos), {, Kluwer Ap-plied Optimization Series. Gilli, M. and E. K˜ellezi, (): The Threshold Heuristic method of optimal generalized hypercube encoding for pictorial databases book Heuristic for Index Tracking.

In Financial Engineering,File Size: 1MB. Heuristic methods for sequence alignment BLAST, BLAT and more. 2 Eitan Rubin & Shmuel Pietrokovski, Advanced Topic in BioinformaticsWeizmann Institute of Science G Database size Q The size of the query sequence A Alphabet size.

P The probability that at least one nolp K-mer will match F The number of unspecific matched K-mers PFile Size: KB. Publications of Dr. Shi-Kuo Chang. PAPERS ( Papers in Total. An asterisk indicates a refereed journal publication, and a dollar sign indicates that the other author is the principal author) A.

Visual Languages and Image Information Systems *A1. design of heuristic methods to solve hard computational search problems.

An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in to describe heuristics to choose heuristics in the context of combinatorial optimisation.

Abstract. The objective of Latin Hypercube Sampling is to determine an effective procedure for sampling from a (possibly correlated) multivariate population to estimate the distribution function (or at least a significant number of moments) of a complicated function of its variables.

The typical application involves a computer-based model in which it Cited by: 7. III ELSEVIER Journal of Operations Management 15 () JOURNAL OF OPERATIONS MANAGEMENT Heuristic, optimal, static, and dynamic schedules when processing times are uncertain Stephen R.

Lawrence a, Edward C. Sewell h a College of Business and Administration, Universio' of Colorado, Boulder, COUSA Department of Mathematics and Cited by: pattern databases, which were ﬁrst developed and implemented for the sliding-tile puzzles, can be generalized and applied to other domains.

The second is that we divide additive pattern databases into two methods, statically- and dynamically-partitioned databases, and compare the two methods experimentally. methods often fail to obtain an optimal solution in reasonable times.

The main aim of heuristic methods, which provide no guarantee of returning an optimal solution (or even near optimal solution), is to find a reasonably good solution within a realistic amount of time [3, 4].

Meta-heuristic algorithms. A new meta-heuristic method, so-called Ray Optimization, is introduced. This is a multi-agent method having a number of particles as the variables.

These agents are considered as rays of light which refract and their direction changes. The method is validated through some well-known mathematical functions and three mechanical by: Fig. 1 A classiﬁcation of hyper-heuristic approaches, according to two dimensions (i) the nature of the heuristic search space, and (ii) the source of feedback during learning.

more general deﬁnition of the term ‘hyper-heuristic’ which is intended to capture the idea of a method for automating the heuristic design and selection process:Cited by: Meaning of (meta)heuristic methods. For optimization, from Wikipedia: In computer science, metaheuristic designates a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

Bioinformatics 2 - Lecture 7 Heuristic methods, clustering and gene feature ﬁnding Heuristics are typically used when there is no known method to ﬁnd an optimal solution, under the given constraints or at all used to query large sequence databases with sequences DNA/ProteinFile Size: 2MB.

These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed.

optimal, and are superior to the best previously published times of O(log ~ n). Index Terms--Parallel algorithms, hypercube computer, convexity, area, perimeter, di- ameter, smallest enclosing rectangle, image analysis, divide-and-conquer.

Reliability based optimization (RBO) is one of the most appropriate methods for structural design under uncertainties. It searches for the best compromise between cost and safety while considering system uncertainties by incorporating reliability measures within the optimization. Despite the advantages of RBO, its application to practical engineering problem is still quite.

More generally, selective encoding suggests that these interactions reflect an active, online evaluation of options during the first encounter.

Furthermore, which properties of the stimulus cause modulations of the gaze bias effect should be those that are task-relevant and useful as a heuristic to make the choice at by: Methods and Techniques in Operation Research A.

SADEGHEIH Department of Industrial Engineering University of Yazd, : IRAN, YAZD Abstract: In this paper, a brief summary of the heuristic methods, single-stage optimization methods, time-phased optimization methods, artificial intelligence (AI) techniques and iterative.

For one heuristic method is greedy, by applying different heuristic methods, we can keep the diversity of columns and avoid getting into a local optimal direction.

Based on the framework of hyper-heuristic [ 14, 22 ], the competition rules are applied to guide the selection of the low-level heuristic during the search by: THE COMBINATION OF HEURISTIC AND META-HEURISTIC ALGORITHM FOR DG SIZING AND SITTING CONCURRENT WITH OPTIMIZING NETWORK CONFIGURATION Akbar Bayat Zanjan Electric Distribution Company – Iran [email protected] ABSTRACT A variety of methods have been proposed for addressing optimal location of Dispersed Generations (DG) in.

There are much research into Artificial Intelligence (AI) and Semantic Web over the past few years and intelligent behaviour such as learning, analysing, problem solving, planning and abstracting is displayed by modern computer systems.

Automatically acquiring domain-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training Author: Hong Qian, Ming Xiang Sui, Yun Fei Jiang, Dong Hui Zhang, Heng Guo. Heuristic optimization algorithms are artificial intelligence search methods that can be used to find the optimal decisions for designing or managing a wide range of complex systems.

This course describes a variety of (meta) heuristic search methods including simulated annealing, tabu search, genetic algorithms, genetic programming, dynamically.

When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP) technique, which is based on the BP neural network algorithm (BP-DHP), has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth.

In this paper, a dual DHP technique based on Extreme Learning Machine (ELM) Author: Hui Li, Yongsui Wen, Wenjie Sun.

Heuristics is simply to use domain knowledge (knowledge about the problem) to speedup the solution. For instance, if you are trying to solve the traveling sales man problem TSP, and you are at city A, then your heuristic could be "next take the closet city to A using aerial distance".

Thanks for contributing an answer to Data Science Stack Exchange. Please be sure to answer the question. Provide details and share your research. But avoid Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience.

Use MathJax to format equations. () Optimal and heuristic solution methods for a multiprocessor machine scheduling problem. () New algorithms for parallelizing relational database joins in the presence of data skew. SIAM Journal on ComputingCited by: 2 EXTERNAL MEMORY ALGORITHMS, I/O EFFICIENCY, AND DATABASES.

A good introduction on external memory algorithms and data structures is my book on the subject. Aggarwal and J. Vitter. ``The Input/Output Complexity of Sorting and Related Problems,'' Communications of the ACM, 31(9), September Optimal and Heuristic Solutions for Codebreaking Games: A comprehensive tutorial to ultimately crack the code for board games and to become an AI game programming mastermind - Kindle edition by Gur, Serkan.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Optimal and Heuristic. () Optimal and heuristic solution methods for a multiprocessor machine scheduling problem.

Computers & Operations Research() Tighter approximation bounds for LPT scheduling in two special by: Exploring Hyper-heuristic Methodologies with Genetic Programming 3 generality at which search methodologies operate.

Introductions to hyper-heuristics can be found in [9, 53]. An important (very well known) observation which guides much hyper-heuristic research is that different heuristics have different strengths and weaknesses. A key. - the study or practice of heuristic procedure - a heuristic method or procedure Heuristics [email protected] Intro Problem Solving in Computer Science © Shaffer & McQuain Heuristic: Wishful Thinking For some problems, you can get to a solution by:File Size: KB.

Posted by Hamid R. Arabnia, AM. With the heuristic algorithm, we can apply a similar analysis. We’ll think of the heuristic algorithm as choosing an ordering on the entire database, both type-A entries and type-B. Then we’ll pull out the type-A entries into a separate list, but keeping the order the same.

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems.

One of the motivations for studying hyper-heuristics is to build .A heuristic is an educated guess which serves as a guide for subsequent explorations. Unlike an algorithm, the results of a heuristic are neither predictable nor reproducible.

A real-world comparison of algorithms and heuristics can be seen in human learning. When a child learns to walk, for example, its approach is heuristic, trying different.I'm an undergraduate engineering student.

I took the course of Optimization this semester, and the heuristic methods. My professor basically says that these methods, PSO for example have no mathematical background and they do not work! I have found nothing online supporting his views on the matter.