Results 1 - 10 of 1,271

Data Mining: Concepts and Techniques

by Jiawei Han, Micheline Kamber , 2000
"... Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific and government transactions and managements, a ..."
Abstract - Cited by 3142 (23 self) - Add to MetaCart
Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific and government transactions and managements

Markov Random Field Models in Computer Vision

by S. Z. Li , 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
Abstract - Cited by 516 (18 self) - Add to MetaCart
. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling

Probabilistic Inference Using Markov Chain Monte Carlo Methods

by Radford M. Neal , 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract - Cited by 736 (24 self) - Add to MetaCart
Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces

Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem

by Mauricio A. Hernández, Salvatore J. Stolfo - DATA MINING AND KNOWLEDGE DISCOVERY , 1998
"... The problem of merging multiple databases of information about common entities is frequently encountered in KDD and decision support applications in large commercial and government organizations. The problem we study is often called the Merge/Purge problem and is difficult to solve both in scale and ..."
Abstract - Cited by 250 (0 self) - Add to MetaCart
and accuracy. Large repositories of data typically have numerous duplicate information entries about the same entities that are difficult to cull together without an intelligent "equational theory" that identifies equivalent items by a complex, domain-dependent matching process. We have developed a

Toward integrating feature selection algorithms for classification and clustering

by Huan Liu, Lei Yu - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005
"... This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals ..."
Abstract - Cited by 267 (21 self) - Add to MetaCart
unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example

Learning to resolve natural language ambiguities: A unified approach

by Dan Roth - In Proceedings of the National Conference on Artificial Intelligence. 806-813. Segond F., Schiller A., Grefenstette & Chanod F.P , 1998
"... distinct semanticonceptsuch as interest rate and has interest in Math are conflated in ordinary text. We analyze a few of the commonly used statistics based The surrounding context- word associations and syn-and machine learning algorithms for natural language tactic patterns in this case- are suffl ..."
Abstract - Cited by 172 (78 self) - Add to MetaCart
- are sufflcicnt to identify disambiguation tasks and observe tha they can bc recast as learning linear separators in the feature space. the correct form. Each of the methods makes a priori assumptions, which Many of these arc important stand-alone problems it employs, given the data, when searching for its hy

Description Logics For Conceptual Data Modeling

by Diego Calvanese, Maurizio Lenzerini, Daniele Nardi , 1998
"... The article aims at establishing a logical approach to class-based data modeling. After a discussion on class-based formalisms for data modeling, we introduce a family of logics, called Description Logics, which stem from research on Knowledge Representation in Arti cial Intelligence. The logics ..."
Abstract - Cited by 143 (22 self) - Add to MetaCart
The article aims at establishing a logical approach to class-based data modeling. After a discussion on class-based formalisms for data modeling, we introduce a family of logics, called Description Logics, which stem from research on Knowledge Representation in Arti cial Intelligence

Contextual Crowd Intelligence

by unknown authors
"... Most data analytics applications are industry/domain specific, e.g., predicting patients at high risk of being admitted to intensive care unit in the healthcare sector or predicting malicious SMSs in the telecommunication sector. Existing solutions are based on “best practices”, i.e., the systems ’ ..."
Abstract - Add to MetaCart
’ decisions are knowledge-driven and/or data-driven. However, there are rules and exceptional cases that can only be precisely formulated and identified by subject-matter experts (SMEs) who have accumulated many years of experience. This paper envisions a more intelligent database management system (DBMS

Unified Government, Bill Approval, and the Legislative Weight of the President

by Eduardo Alemán, Ernesto Calvo
"... This article proposes a new approach to measuring the legislative weight of the president and Congress based on the approval of each actor’s legislative agenda. The authors focus on presidential systems where presidents possess both formal authority to introduce their own bills and a variety of prer ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
empirically test their propositions using data from Argentina. The results indicate that the policy and productivity weights of the president actually increase in the absence of unified government. Keywords Congress, divided government, legislative success Most of the comparative research on presidential

More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing.

by Ryan S J D Baker , Albert T Corbett , Vincent Aleven - In: Woolf, B., Aimeur, E., Nkambou, R., Lajoie, S. (Eds.) ITS , 2008
"... Abstract. Modeling students' knowledge is a fundamental part of intelligent tutoring systems. One of the most popular methods for estimating students' knowledge is Corbett and Anderson's [6] Bayesian Knowledge Tracing model. The model uses four parameters per skill, fit using student ..."
Abstract - Cited by 84 (23 self) - Add to MetaCart
Abstract. Modeling students' knowledge is a fundamental part of intelligent tutoring systems. One of the most popular methods for estimating students' knowledge is Corbett and Anderson's [6] Bayesian Knowledge Tracing model. The model uses four parameters per skill, fit using
Results 1 - 10 of 1,271