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Minimum Error Rate Training in Statistical Machine Translation

by Franz Josef Och , 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
Abstract - Cited by 757 (7 self) - Add to MetaCart
Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training

A direct approach to false discovery rates

by John D. Storey , 2002
"... Summary. Multiple-hypothesis testing involves guarding against much more complicated errors than single-hypothesis testing. Whereas we typically control the type I error rate for a single-hypothesis test, a compound error rate is controlled for multiple-hypothesis tests. For example, controlling the ..."
Abstract - Cited by 775 (14 self) - Add to MetaCart
the false discovery rate FDR traditionally involves intricate sequential p-value rejection methods based on the observed data. Whereas a sequential p-value method fixes the error rate and estimates its corresponding rejection region, we propose the opposite approach—we fix the rejection region

Improved Boosting Algorithms Using Confidence-rated Predictions

by Robert E. Schapire , Yoram Singer - MACHINE LEARNING , 1999
"... We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find impr ..."
Abstract - Cited by 940 (26 self) - Add to MetaCart
a third method based on output coding. One of these leads to a new method for handling the single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.

Instance-based learning algorithms

by David W. Aha, Dennis Kibler, Marc K. Albert - Machine Learning , 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
Abstract - Cited by 1389 (18 self) - Add to MetaCart
to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances

Empirical exchange rate models of the Seventies: do they fit out of sample?

by Richard A. Meese, Kenneth Rogoff - JOURNAL OF INTERNATIONAL ECONOMICS , 1983
"... This study compares the out-of-sample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and trade-weighted dollar exch ..."
Abstract - Cited by 854 (12 self) - Add to MetaCart
exchange rates. The candidate structural models include the flexible-price (Frenkel-Bilson) and sticky-price (Dornbusch-Frankel) monetary models, and a sticky-price model which incorporates the current account (Hooper-Morton). The structural models perform poorly despite the fact that we base

A study of translation edit rate with targeted human annotation

by Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, John Makhoul - In Proceedings of Association for Machine Translation in the Americas , 2006
"... We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform to cha ..."
Abstract - Cited by 583 (9 self) - Add to MetaCart
We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform

Neural Network-Based Face Detection

by Henry A. Rowley, Shumeet Baluja, Takeo Kanade - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1998
"... We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present ..."
Abstract - Cited by 1206 (22 self) - Add to MetaCart
We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present

Equation-based congestion control for unicast applications

by Sally Floyd , Mark Handley , Jitendra Padhye , Jörg Widmer - SIGCOMM '00 , 2000
"... This paper proposes a mechanism for equation-based congestion control for unicast traffic. Most best-effort traffic in the current Internet is well-served by the dominant transport protocol, TCP. However, traffic such as best-effort unicast streaming multimedia could find use for a TCP-friendly cong ..."
Abstract - Cited by 830 (29 self) - Add to MetaCart
This paper proposes a mechanism for equation-based congestion control for unicast traffic. Most best-effort traffic in the current Internet is well-served by the dominant transport protocol, TCP. However, traffic such as best-effort unicast streaming multimedia could find use for a TCP

Text Chunking using Transformation-Based Learning

by Lance A. Ramshaw, Mitchell P. Marcus , 1995
"... Eric Brill introduced transformation-based learning and showed that it can do part-ofspeech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive "baseNP" chunks. For ..."
Abstract - Cited by 523 (0 self) - Add to MetaCart
. For this purpose, it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word. In automatic tests using Treebank-derived data, this technique achieved recall and precision rates of roughly 92% for baseNP chunks and 88% for somewhat more complex chunks

Semantic similarity based on corpus statistics and lexical taxonomy

by Jay J. Jiang, David W. Conrath - Proc of 10th International Conference on Research in Computational Linguistics, ROCLING’97 , 1997
"... This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantifie ..."
Abstract - Cited by 873 (0 self) - Add to MetaCart
calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human
Results 1 - 10 of 122,623