How can the health care of United States citizens best be improved? : a collection of excerpts and bibliography relating to the high school debate topic, 1977-1978, pursuant to Public Law 88-246 /

Mode of access: Internet.

Word correlation and automatic indexing, phase IIA final report, C82-2UA. Submitted to the Council on Library Resources.

Includes bibliography.

Indexing; principles, rules and examples.

Also issued, with Library school bulletins 20 and 23, as appendix 3, of the State library report for 1905.

Colloquial phrases and dialogues in German and English : on every topic necessary to maintain conversation; with directions for pronunciation /

Cover: Revised ed.

A collection of colloquial phrases, on every topic necessary to maintain conversation : arranged under different heads; with numerous remarks on the peculiar pronunciation and use of various words. The whole so disposed as considerably to facilitate the acquisition of a correct pronunciation of the French /

Back cover contains advertisement of works published by the author.

The philosophy of reform : a lecture delivered before the Berean Institute, in the Broadway Tabernacle, New York, Jan. 20, 1843 ; with four discourses upon the same general topic, delivered in New York and Brooklyn /

Mode of access: Internet.

A collection of colloquial phrases on every topic necessary to maintain conversation ; arranged under different heads ; with numerous remarks on the peculiar pronunciation and use of various words ; the whole so disposed as considerably to facilitate the acquisition of a correct pronunciation of the French /

Mode of access: Internet.

Water resources thesaurus; a vocabulary for indexing and retrieving the literature of water resources research and development.

"Developed under a contract with the Science Information Exchange (SIE), Smithsonian Institution."

Indexing with the consumer price index : problems and alternatives /

"June 1981"--cover

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Indexing and integrating multiple features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index image's multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partition's center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images have similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the dimensionality curse existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms image's text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partition's center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. To effectively integrate multi-features, we also investigated the following evidence combination techniques-Certainty Factor, Dempster Shafer Theory, Compound Probability, and Linear Combination. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude. And Certainty Factor and Dempster Shafer Theory perform best in combining multiple similarities from corresponding multiple features.

Indexing text and visual features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index imagersquos multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partitionrsquos center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images haves similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the ldquodimensionality curserdquo existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms imagersquos text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partitionrsquos center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude.