Sequential search

Xem 1-15 trên 15 kết quả Sequential search
  • Sequential Search In an unordered list In an ordered list Binary Search Forgetful Version Recognizing Equality Comparison Tree Linked List vs. Contiguous List .Searching We are given a list of records. Each record is associated with a key. We are given one key (target), and are asked to search the list to find the record(s) whose key is the same as the target. May be more than one record with the same key.

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  • Bài giảng "Cấu trúc dữ liệu và giải thuật - Chương 7: Tìm kiếm" trình bày các khái niệm về tìm kiếm, bản ghi và khóa, bản ghi và khóa trên C++, tìm tuần tự (sequential search), tìm trên danh sách có thứ tự, quản lý danh sách có thứ tự, thêm vào danh sách có thứ tự - Giải thuật, tìm nhị phân, cây so sánh của giải thuật,... Mời các bạn cùng tham khảo nội dung chi tiết.

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  • Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming.

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  • These estimates were obtained by asking respondents several questions about their current health insurance coverage. Separate questions were asked about Medicare, Wisconsin Medicaid, BadgerCare Plus, private health insurance, employer-sponsored health insurance and other kinds of health care coverage for each household member. Those without any current health care coverage were considered uninsured at the time of the interview. (See Table 5, page 18, for specific types of health insurance coverage.

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  • The crucial assumption that allows as to solve the two-good model relatively simply is the ability of the shoppers to combine purchases from two stores without incurring additional cost. This assumption can be justi ed for internet shopping or the cases when as Stahl, II (1989) puts it \Casual empiricism suggests that there is a non-negligible measure of consumers who seem to derive enjoyment from shopping itself".

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  • Financial data are conventionally represented in numeric format for data mining purpose. However, recent works have demonstrated promising results of representing financial data symbolically. For an instance, Kovalerchuk et al. (2002) argues that symbolic relational data mining is more suitable in incorporating background knowledge. Their proposed methodology outperforms numeric financial data in generating IF-Then rules. In (Ting et al.

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  • Basic Concepts • Sequential search: O(n) • Binary search: O(log2n) Requiring several key comparisons before the target is found • Search complexity: Size 16 50 256 1,000 10,000 100,000 1,000,000 Binary 4 6 8 10 14 17 20 Sequential (Average) 8 25 128 500 5,000 50,000 500,000 Sequential (Worst Case) 16 50 256 1,000 10,000 100,000 1,000,000 • Is there a search algorithm whose complexity is O(1)? • Is there a search algorithm whose complexity is O(1)? YES.

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  • Lecture Data Structures & Algorithms: Chapter 3 (Searching Techniques prens) presented linear (sequential) search, binary search, complexity of algorithms.

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  • In this section, we study operations that, in one way or another, process their data sequentially. Examples include searching on one or more unindexed attributes, creation of bitmaps based on selection conditions, and scalar aggrega- tion. Grouped aggregation can be achieved by a combina- tion of sorting followed by one scalar aggregation per group. Scan-like operations are also used as components of more complex operations that we will discuss in Section 4 and Section 5.

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  • In [2], the problem of discovering sequential patterns over large databases of customer transactions is considered. The proposed algorithms generate a data sequence for each customer from the database and search on this set of se- quences for a frequent sequential pattern. For example, the algorithms can discover that customers typically rent “Star Wars,” then “Empire Strikes Back,” and then “Return of the Jedi.

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  • The need to search for complex and recurring patterns in database sequences is shared by many applications. In this paper, we investigate the design and optimization of a query language capable of expressing and supporting efficiently the search for complex sequential patterns in database systems. Thus, we first introduce SQL-TS, an extension of SQL to express these patterns, and then we study howto optimize the queries for this language.We take the optimal text search algorithmof Knuth, Morris and Pratt, and generalize it to handle complex queries on sequences.

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  • Assisted direct manipulation interfaces have been widely studied in computer graphics, dating back to Ivan Sutherland’s Sketch- Pad [Sutherland 1963]. In the context of architectural design, Harada et al. [1995] describe an interface for creating floorplans. Their interface supports local constraints and invokes discrete lo- cal search whenever the user drives the layout into a challeng- ing configuration. Likewise, Michalek and Papalambros [2002] use sequential quadratic programming to optimize an arrangement of rectangles in response to interactive manipulation.

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  • However, the implementation and optimization techniques needed to satisfy the special (update and transaction) requirements of active databases are not present in sequence queries, which therefore provide greater opportunities for query optimization, which are discussed next. In this article, we explore optimization techniques inspired by string-search algorithms, since finding sequential patterns in databases is somewhat sim- ilar to finding phrases in text.

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  • This paper describes a new hardware algorithm for morpheme extraction and its implementation on a specific machine (MEX-I), as the first step toward achieving natural language parsing accelerators. It also shows the machine's performance, 100-1,000 times faster than a personal computer. This machine can extract morphemes from 10,000 character Japanese text by searching an 80,000 morpheme dictionary in I second. It can treat multiple text streams, which are composed of character candidates, as well as one text stream.

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  • In computer science, a balanced trees is a self-balancing tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. In this topic, we will: Introduce the idea of balance and a few examples.

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