ICML 1999 - PDF Preprint Papers

ICML 1999 Workshop on Statistical Network Analysis

Teaser Browsing | Click Here

Title Browsing | Click Here

Wall of Fame | Most Viewed ICML-1999 Paper

click to vote

ICML
1999
IEEE

469views Machine Learning» more ICML 1999»

AdaCost: Misclassification Cost-Sensitive Boosting

14 years 5 months ago

Download www.weifan.info

AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...

Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip...

claim paper

Read More »

Disclaimer and Copyright Notice

Sciweavers respects the rights of all copyright holders and in this regard, authors are only allowed to share a link to their preprint paper on their own website. Every contribution is associated with a desciptive image. It is the sole responsibility of the authors to ensure that their posted image is not copyright infringing. This service is compliant with IEEE copyright.

Id	Views	Title	Status

1	469	AdaCost: Misclassification Cost-Sensitive Boosting	claim
2	217	The Alternating Decision Tree Learning Algorithm	claim
3	210	Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees	claim
4	195	Feature Engineering for Text Classification	claim
5	191	Active Learning for Natural Language Parsing and Information Extraction	claim
6	168	Least-Squares Temporal Difference Learning	claim
7	163	Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes	claim
8	159	Abstracting from Robot Sensor Data using Hidden Markov Models	claim
9	156	Approximation Via Value Unification	claim
10	152	Distributed Value Functions	claim
11	138	Using Reinforcement Learning to Spider the Web Efficiently	claim
12	129	Implicit Imitation in Multiagent Reinforcement Learning	claim
13	114	Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples	claim
14	114	Machine-Learning Applications of Algorithmic Randomness	claim
15	112	Learning Hierarchical Performance Knowledge by Observation	claim
16	108	Detecting Motifs from Sequences	claim
17	107	Learning Policies with External Memory	claim
18	102	Experiments with Noise Filtering in a Medical Domain	claim
19	97	Making Better Use of Global Discretization	claim
20	97	Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping	claim
21	87	Hierarchical Models for Screening of Iron Deficiency Anemia	claim

Sciweavers

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers