#### Filter Results:

- Full text PDF available (64)

#### Publication Year

1988

2016

- This year (0)
- Last 5 years (16)
- Last 10 years (45)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

#### Method

#### Organism

Learn More

Boosting is a general method for improving the accuracy of any given learning algorithm. This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting's relationship to support-vector machines. Some… (More)

- Naoki Abe, Hiroshi Mamitsuka
- ICML
- 1998

We address the problem of automatically acquiring case frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the ef-ciency, our… (More)

- Bianca Zadrozny, John Langford, Naoki Abe
- ICDM
- 2003

We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conversion is based on cost-proportionate weighting of the training examples, which can be realized either by feeding the weights to the classification algorithm (as often done in… (More)

We consider the problem of learning co-occurrence information between two word categories, or more in general between two discrete random variables taking values in a hierarchically classified domain. In particular , we consider the problem of learning the 'as-sociation norm' defined by A(x, y) = p(x, y)/p(x)p(y), where p(x, y) is the joint distribution for… (More)

- Naoki Abe, Bianca Zadrozny, John Langford
- KDD
- 2004

Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification. In this paper, we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm. This algorithm is derived using hree key ideas: 1) iterative weighting; 2)… (More)

- Andrew Arnold, Yan Liu, Naoki Abe
- KDD
- 2007

The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data, which raises the challenge of how best to leverage the temporal information for causal modeling. Recently graphical modeling with the concept of "Granger causality", based on the… (More)

- Edwin P. D. Pednault, Naoki Abe, Bianca Zadrozny
- KDD
- 2002

Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have been developed that are effective at optimizing cost-sensitive decisions when each decision is considered in isolation. However, the issue of sequential decision making, with the… (More)

- Aurelie C. Lozano, Hongfei Li, +4 authors Naoki Abe
- KDD
- 2009

Attribution of climate change to causal factors has been based predominantly on simulations using physical climate models, which have inherent limitations in describing such a complex and chaotic system. We propose an alternative, data centric, approach that relies on actual measurements of climate observations and human and natural forcing factors.… (More)

We address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. We view this problem as that of estimating a joint probability distribution specifying the joint probabilities of word pairs, such as noun verb pairs. We propose an… (More)