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Results 1 - 10
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213
The Determinants of Credit Spread Changes.
- Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
Abstract
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Cited by 422 (2 self)
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, and maturity groups. Note that this result by itself is not surprising, since theory predicts that all credit spreads should be affected by aggregate variables such as changes in the interest rate, changes in business climate, changes in market volatility, etc. The particularly surprising aspect of our results
Mining Concept-Drifting Data Streams Using Ensemble Classifiers
, 2003
"... Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two ch ..."
Abstract
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Cited by 280 (37 self)
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Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two
The End of an Architectural Era (It's Time for a Complete Rewrite
- Proceedings of the 31st international
, 2005
"... In previous papers [SC05, SBC+07], some of us predicted the end of “one size fits all ” as a commercial relational DBMS paradigm. These papers presented reasons and experimental evidence that showed that the major RDBMS vendors can be outperformed by 1-2 orders of magnitude by specialized engines in ..."
Abstract
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Cited by 200 (23 self)
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in the data warehouse, stream processing, text, and scientific database markets. Assuming that specialized engines dominate these markets over time, the current relational DBMS code lines will be left with the business data processing (OLTP) market and hybrid markets where more than one kind of capability
A general framework for mining concept-drifting data streams with skewed distributions
- In Proc. SDM’07
, 2007
"... In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but cannot handle well rather skewed (e.g., few positives but lots of negatives) and stochastic distributions, which are typ ..."
Abstract
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Cited by 47 (6 self)
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In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but cannot handle well rather skewed (e.g., few positives but lots of negatives) and stochastic distributions, which
Knowledge Maintenance on Data Streams with Concept Drifting
"... Abstract. Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning approach or ensemble classifiers approach. However, both of them can not make a prediction at any time exactly. I ..."
Abstract
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Cited by 1 (0 self)
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Abstract. Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning approach or ensemble classifiers approach. However, both of them can not make a prediction at any time exactly
A Framework for Diagnosing Changes in Evolving Data Streams
- ACM SIGMOD Conference
, 2003
"... In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. This results in databases which grow without limit at a rapid rate. This data can often show important changes in trends over time. In such cases, it i ..."
Abstract
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Cited by 73 (12 self)
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In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. This results in databases which grow without limit at a rapid rate. This data can often show important changes in trends over time. In such cases
StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams
, 2004
"... We demonstrate StreamMiner, a random decision-tree ensemble based engine to mine data streams. A fundamental challenge in data stream mining applications (e.g., credit card transaction authorization, security buysell transaction, and phone call records, etc) is concept-drift or the discrepancy betwe ..."
Abstract
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Cited by 21 (2 self)
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We demonstrate StreamMiner, a random decision-tree ensemble based engine to mine data streams. A fundamental challenge in data stream mining applications (e.g., credit card transaction authorization, security buysell transaction, and phone call records, etc) is concept-drift or the discrepancy
Spatial Prediction of Stream Temperatures Using Top-Kriging with an External Drift
, 2011
"... Abstract Top-kriging is a method for estimating stream flow and stream flow-related variables on a river network. Top-kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The top-kriging weights are estimated by a family of variogram models ..."
Abstract
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Cited by 2 (0 self)
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models (regularisations) for different catchment areas (kriging support), which accounts for the different scales and the nested nature of the catchments. This assures that kriging weights are distributed to both hydrologically connected and unconnected sites of the stream network according to the data
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
- In ICDM
, 2004
"... This paper considers the problem of mining closed frequent itemsets over a sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory const ..."
Abstract
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Cited by 77 (4 self)
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selected set of itemsets over a sliding-window. The selected itemsets consist of a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non
Mining Concept Drift from Data Streams by Unsupervised Learning
"... Mining is involved with knowing the unknown characteristics from the databases or gaining of Knowledge (Knowledge Discovery) from Databases to get more useful information from the database. Real time databases which are constantly changing with time, there may arise a point when traditional Data ..."
Abstract
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. This phenomenon is known as Concept Drift. The meaning of Concept Drift is the statistical properties of the target variable, i.e. how the properties of the target variable change over the course of time. The basic idea behind the ―Mining Concept Drift from Data Stream by Unsupervised Learning ‖ is to detect
Results 1 - 10
of
213
