Integrating E-Commerce and Data Mining: (2002)
Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based...
Generalization Error of Combined Classifiers (2002)
Llew Mason, Peter L. Bartlett, Mostefa Golea
this paper we present an upper bound on the generalization error of any thresholded convex combination of functions which are themselves thresholded convex combinations of functions in terms of the...
Generalization in Threshold Networks, Combined Decision Trees and Combined Mask Perceptrons (2002)
Llew Mason, Peter Bartlett, Mostefa Golea
We derive an upper bound on the generalization error of classifiers from a certain class of threshold networks. The bound depends on the margin of the classifier and the average complexity of the...
Boosting Algorithms as Gradient Descent (2002)
Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean
Much recent attention, both experimental and theoretical, has been focussed on classification algorithms which produce voted combinations of classifiers. Recent theoretical work has shown that the...
Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based...
The Alternating Decision Tree Learning Algorithm (2002)
The application of boosting procedures to decision tree algorithms has been shown to produce very accurate classifiers. These classifiers are in the form of a majority vote over a number of decision...
Margins and Combined Classifiers (2002)
Many binary classification algorithms produce real-valued predictions which are then thresholded to produce a binary classification. These real-valued predictions can often be viewed as a measure of...
Error Bounds for Voting Classifiers Using Margin Cost Functions (2002)
Recent theoretical results haveshown that the accuracy of thresholded real-valued functions (suchasvoting classifiers) is greatly improved if the underlying function has large margins on the training...
Direct Optimization of Margins Improves Generalization in Combined Classifiers (2002)
Llew Mason, Peter Bartlett, Jonathan Baxter
Sonar Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm.
Appeared in ICDM'01: The 2001 IEEE International Conference on Data Mining (2002)
Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng
We show that the e-commerce domain can provide all the right ingredients for successful data mining. We describe an integrated architecture for supporting this integration. The architecture can...
Real World Performance of Association Rule Algorithms (2001)
Zijian Zheng, Ron Kohavi, Llew Mason, Blue Martini Software
This study compares five well-known association rule algorithms using three real-world datasets and an artificial dataset. The experimental results confirm the performance improvements previously...
Real World Performance of Association Rule Algorithms (2001)
Zijian Zheng, Ron Kohavi, Llew Mason, Blue Martini Software
Association rule discovery has been an active research area over the past few years with several new proposals for algorithms that improve the running time for generating association rules or...
Direct Optimization of Margins Improves Generalization in Combined Classifiers (2001)
Llew Mason, Peter Bartlett, Jonathan Baxter
0 0 1 Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm. The dark curve is AdaBoost, the light curve is DOOM. DOOM sacrifices...
Integrating E-Commerce and Data Mining: Architecture and Challenges (2000)
Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng, Blue Martini Software
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based...
Integrating E-Commerce and Data Mining: Architecture and Challenges (2000)
Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based...
Integrating E-Commerce and Data Mining: Architecture and Challenges (2000)
Ansari, Suhail, Kohavi, Ron, Mason, Llew, Zheng, Zijian
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based...
Integrating E-Commerce and Data Mining: Architecture and Challenges (2000)
Suhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based...
Boosting Algorithms as Gradient Descent (2000)
Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean
We provide an abstract characterization of boosting algorithms as gradient descent on cost-functionals in an inner-product function space. We prove convergence of these functional-gradient-descent...
Generalization Error of Combined Classifiers (1999)
Llew Mason, Peter L. Bartlett, Mostefa Golea
this paper we present an upper bound on the generalization error of any thresholded convex combination of functions which are themselves thresholded convex combinations of functions in terms of the...
Boosting Algorithms as Gradient Descent (1999)
Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean
Much recent attention, both experimental and theoretical, has been focussed on classification algorithms which produce voted combinations of classifiers. Recent theoretical work has shown that the...
Boosting Algorithms as Gradient Descent in Function Space (1999)
Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean
Much recent attention, both experimental and theoretical, has been focussed on classification algorithms which produce voted combinations of classifiers. Recent theoretical work has shown that the...
Direct Optimization of Margins Improves Generalization in Combined Classifiers (1999)
Llew Mason, Peter Bartlett, Jonathan Baxter
0 0 1 Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm. The dark curve is AdaBoost, the light curve is DOOM. DOOM sacrifices...
Generalization in Threshold Networks, Combined Decision Trees and Combined Mask Perceptrons (1999)
Llew Mason, Peter Bartlett, Mostefa Golea
We derive an upper bound on the generalization error of classifiers from a certain class of threshold networks. The bound depends on the margin of the classifier and the average complexity of the...
Direct Optimization of Margins Improves Generalization in Combined Classifiers (1998)
Llew Mason, Peter Bartlett, Jonathan Baxter
0 0 1 Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm. The dark curve is AdaBoost, the light curve is DOOM. DOOM sacrifices...
Direct Optimization of Margins Improves Generalization in Combined Classifiers (1998)
Llew Mason, Peter Bartlett, Jonathan Baxter
0 0 1 Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm. The dark curve is AdaBoost, the light curve is DOOM. DOOM sacrifices...