62599943
Finding nuggets in documents: A machine learning approach
Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of hissher interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective. © 2006 Wiley Periodicals, Inc.
3764367
eLearning assessment through textual analysis of class discussions
Most of the elearning classes use discussion forums to support class interaction. This paper presents a novel approach that automatically assesses student class performance by analyzing knowledge density in online class messages. Class performance of students is evaluated from three perspectives: the quality of their course work, the quantity of their efforts, and the activeness of their participation; three measures - knowledge density, message length, and message count, are derived from class messages respectively. Together, they are used to calculate a performance indicator score. The experiment shows that there is a high correlation between the performance indicator scores and the actual grades assigned by instructors. The rank orders of students by the performance indicator score are highly correlated with those by the actual grades as well.
3822993
Learning by Examples: Identifying Key Concepts from Text Using Pre-Defined Inputs
3223686
Personalized Knowledge Discovery: Mining Novel Association Rules from Text
This paper presents a methodology for personalized knowledge discovery from text. It derives a user’s background knowledge from his/her background documents, and exploits such knowledge to evaluate the novelty of discovered knowledge in the form of association rules by measuring the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment results show that the proposed user-oriented novelty measure is highly correlated with the human subjective rule novelty and usefulness ratings. It outperforms seven major objective interestingness measures and the WordNet novelty measure for identifying novel and useful rules.
3822190
Improving Concept Hierarchy Development for Web Returned Documents Using Automatic Classification
3781215
Web mining from competitors' websites
This paper presents a framework for user-oriented text mining. It is then illustrated with an example of discovering knowledge from competitors' websites. The knowledge to be discovered is in the form of association rules. A user's background knowledge is represented as a concept hierarchy developed from documents on his/her own website. The concept hierarchy captures the semantic usage of words and relationships among words in background documents. Association rules are identified among the noun phrases extracted from documents on competitors' websites. The interestingness measure, i.e. novelty, which measures the semantic distance between the antecedent and the consequent of a rule in the background knowledge, is computed from the co-occurrence frequency of words and the connection lengths among words in the concept hierarchy. A user evaluation of the novelty of discovered rules demonstrates that the correlation between the algorithm and the human judges is comparable to that between human judges.
14274462
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Algorithm based clustering method to reduce the search space. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm is demonstrated in comparative studies.
3226359
Segmentation, Registration, and Deformation Analysis of 3D MR Images of Mice
We demonstrate our mouse MR image and shape analysis pipeline. The long term goal of our work is the description of structural shape variations in normal, genetically identical mice and the subsequent detection of pathological phenotypes in genetically modified mice. The pipeline begins with the acquisition and reconstruction of high resolution MR images of mice. Regions of the images containing organs under study are enhanced via edge-preserving smoothing and mouse organs are segmented using a deformable mesh model. Images are registered using a multi-resolution approach and displacement fields are used to calculate local volume changes. Detailed descriptions and results for each step of the pipeline are presented.
8954904
Modification of the AdaBoost-based Detector for Partially Occluded Faces
While face detection seems a solved problem under general conditions, most state-of-the-art systems degrade rapidly when faces are partially occluded by other objects. This paper presents a solution to detect partially occluded faces by reasonably modifying the AdaBoost-based face detector. Our basic idea is that the weak classifiers in the AdaBoost-based face detector, each corresponding to a Haar-like feature, are inherently a patch-based model. Therefore, one can divide the whole face region into multiple patches, and map those weak classifiers to the patches. The weak classifiers belonging to each patch are re-formed to be a new classifier to determine if it is a valid face patch - without occlusion. Finally, we combine all of the valid face patches by assigning the patches with different weights to make the final decision whether the input subwindow is a face. The experimental results show that the proposed method is promising for the detection of occluded faces
15165209
A PCA-Based Vehicle Classification Framework
Due to its great practical importance, Intelligent Transportation System has been an active research area in recent years. In this paper, we present a framework that incorporates various aspects of an intelligent transportation system with its ultimate goal being vehicle classification. Given a traffic video sequence, the proposed system first proceeds to segment individual vehicles. Then the extracted vehicle objects are normalized so that all vehicles are aligned along the same direction and measured at the same scale. Following the preprocessing step, two classification algorithms - Eigenvehicle and PCA-SVM, are proposed and implemented to classify vehicle objects into trucks, passenger cars, vans, and pick-ups. These two methods exploit the distinguishing power of Principal Component Analysis (PCA) at different granularities with different learning mechanisms. Experiments are conducted to compare these two methods and the results demonstrate the effectiveness of the proposed framework.
11357832
A latent semantic indexing based method for solving multiple instance learning problem in region-based image retrieval
Relevance feedback (RF) is a widely used technique in incorporating user's knowledge with the learning process for content-based image retrieval (CBIR). As a supervised learning technique, it has been shown to significantly increase the retrieval accuracy. However, as a CBIR system continues to receive user queries and user feedbacks, the information of user preferences across query sessions are often lost at the end of search, thus requiring the feedback process to be restarted for each new query. A few works targeting long-term learning have been done in general CBIR domain to alleviate this problem. However, none of them address the needs and long-term similarity learning techniques for region-based image retrieval. This paper proposes a latent semantic indexing (LSI) based method to utilize users' relevance feedback information. The proposed region-based image retrieval system is constructed on a multiple instance learning (MIL) framework with one-class support vector machine (SVM) as its core. Experiments show that the proposed method can better utilize users' feedbacks of previous sessions, thus improving the performance of the learning algorithm (one-class SVM).