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Fast learning of document ranking functions with the committee perceptron

Jonathan L. Elsas, Vitor R. Carvalho, Jaime G. Carbonell
2008 Proceedings of the international conference on Web search and web data mining - WSDM '08  
We apply this agorithm to the problem of learning ranking functions for document retrieval, known as the "Learning to Rank" problem.  ...  This paper presents a new variant of the perceptron algorithm using selective committee averaging (or voting).  ...  CONCLUSION & FUTURE WORK In this paper we present a new variant of the perceptron algorithm, the committee perceptron, and applied it to the problem of learning document ranking functions.  ... 
doi:10.1145/1341531.1341542 dblp:conf/wsdm/ElsasCC08 fatcat:jz6fzvtbqfezffbcja3e6luhdu

Robust Online Learning to Rank via Selective Pairwise Approach Based on Evaluation Measures

Yoshihiko Suhara, Jun Suzuki, Ryoji Kataoka
2013 Transactions of the Japanese society for artificial intelligence  
The basic strategy of our method is to select the most effective document pair to minimize the objective function using an entered query present in the training data, and then updates the current weight  ...  vector by using only the selected document pair instead of using all document pairs in the query.  ...  Perceptron and the batch learning to rank algorithm Rank-ingSVM.  ... 
doi:10.1527/tjsai.28.22 fatcat:umoorrpd4fgcjcj4oagh3xos3e

Online learning from click data for sponsored search

Massimiliano Ciaramita, Vanessa Murdock, Vassilis Plachouras
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
Furthermore, we find that online multilayer perceptron learning, based on a small set of features representing content similarity of different kinds, significantly outperforms an information retrieval  ...  We investigate the sponsored search problem from a machine learning perspective with respect to three main sub-problems: how to use click data for training and evaluation, which learning framework is more  ...  To improve the expressive power of our ranking functions, within the online perceptron approach, we experimented also with multilayer models.  ... 
doi:10.1145/1367497.1367529 dblp:conf/www/CiaramitaMP08 fatcat:qmgw72nkgbgrvld3afm7ybsl3a

Recall Systems: Effcient Learning and Use of Category Indices

Omid Madani, Wiley Greiner, David Kempe, Mohammad R. Salavatipour
2007 Journal of machine learning research  
We introduce the framework of recall systems for efficient learning and retrieval of categories when the number of categories is large.  ...  In our experiments, the index is learned within minutes, and reduces the number of categories by several orders of magnitude, without affecting the quality of classification overall.  ...  Acknowledgements We thank Thomas Pierce, Dennis DeCoste, and Kevin Lang for pointers, discussions, or assistance, and the anonymous reviewers for their comments and suggestions.  ... 
dblp:journals/jmlr/MadaniGKS07 fatcat:lsnf2ple2rfohdfuptrmrtxwva

A Survey of Handwritten Character Recognition with MNIST and EMNIST

Alejandro Baldominos, Yago Saez, Pedro Isasi
2019 Applied Sciences  
To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely  ...  deep learning techniques over this dataset.  ...  [78] , where they proposed the use of deep convolutional extreme learning machine, where gradient descent is not used to train the network, allowing a very fast learning stage (only 21 min using CPU,  ... 
doi:10.3390/app9153169 fatcat:ifyi7crq3fdgtmowyojetsukte

Large-Scale Many-Class Learning [chapter]

Omid Madani, Michael Connor
2008 Proceedings of the 2008 SIAM International Conference on Data Mining  
On problems with hundreds of thousands of instances and thousands of categories, the index is learned in minutes, while other methods can take orders of magnitude longer.  ...  The candidates can then be ranked and the ranking or the scores can be used for category assignment. We present novel online index learning algorithms.  ...  Acknowledgements Many thanks to Dennis DeCoste, Scott Gaffney, Sathiya Keerthy, John Langford, Chih-Jen Lin, Kishore Papineni, Lance Riedel, and the machine learning group at Yahoo!  ... 
doi:10.1137/1.9781611972788.76 dblp:conf/sdm/MadaniC08 fatcat:tfgy3lwornftxoq3b2av4gvj44

Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey

Ebru KARAAHMETOĞLU, Süleyman ERSÖZ, Ahmet Kürşad TÜRKER, Volkan ATEŞ, Ali Firat İNAL
2021 Journal of Polytechnic  
Multi Layer Perceptron (MLP) Perceptron is the mathematical function that models a neuron in the simplest way. Perceptron consists of one or more input values, processor and one output value.  ...  The input function of perceptron algorithm is shown in Figure 5 . In a generalized perceptron algorithm, the total value is found by adding the products of n input values and weights.  ...  CONFLICT OF INTEREST There is no conflict of interest in this study.  ... 
doi:10.2339/politeknik.985534 fatcat:67pqkybk25g2tns34bmkbfkrnm

SOLAR: Scalable Online Learning Algorithms for Ranking

Jialei Wang, Ji Wan, Yongdong Zhang, Steven Hoi
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)  
To overcome the limitations, this paper presents SO-LAR -a new framework of Scalable Online Learning Algorithms for Ranking, to tackle the challenge of scalable learning to rank.  ...  We conduct extensive empirical studies by comparing our SOLAR algorithms with conventional learning to rank algorithms on benchmark testbeds, in which promising results validate the efficacy and scalability  ...  Acknowledgments This work was supported by Singapore MOE tier 1 research grant (C220/MSS14C003) and the National Nature Science Foundation of China (61428207).  ... 
doi:10.3115/v1/p15-1163 dblp:conf/acl/WangWZH15 fatcat:vx62h4zrijcbvb6bucqn2m73fq

A learning rule for very simple universal approximators consisting of a single layer of perceptrons

Peter Auer, Harald Burgsteiner, Wolfgang Maass
2008 Neural Networks  
approximators for arbitrary continuous functions with values in [0, 1] if one views the fraction of perceptrons that output 1 as the analog output of the parallel perceptron.  ...  In spite of their simplicity, such circuits can compute any Boolean function if one views the majority of the binary perceptron outputs as the binary output of the parallel perceptron, and they are universal  ...  Note that the output value of this gate grows with the rank of x n in the linear order of the numbers {x 1 , . . . , x n }. Theorem 2.1.  ... 
doi:10.1016/j.neunet.2007.12.036 pmid:18249524 fatcat:75kvkyccgnfqhpyib6u3dk754e

Rank learning for factoid question answering with linguistic and semantic constraints

Matthew W. Bilotti, Jonathan Elsas, Jaime Carbonell, Eric Nyberg
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
This work presents a general rank-learning framework for passage ranking within Question Answering (QA) systems using linguistic and semantic features.  ...  The framework supports the checking of constraints of arbitrary length relating any number of keywords.  ...  The Committee Perceptron algorithm is a generalization of previous Perceptron variants [5] , and is adapted for learning ranking functions based on preferences between pairs of judged passages.  ... 
doi:10.1145/1871437.1871498 dblp:conf/cikm/BilottiECN10 fatcat:3kvhb33w3fh3lbijolegmsm7qq

Linguistic and semantic passage retrieval strategies for question answering

Matthew W. Bilotti
2011 SIGIR Forum  
Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers.  ...  This thesis proposes two linguistic and semantic passage retrieval methods for QA, one based on structured retrieval and the other on rank-learning techniques.  ...  See Committee Perceptron The Committee Perceptron is an on-line algorithm for learning linear ranking functions for learning-to-rank tasks [16] .  ... 
doi:10.1145/1924475.1924495 fatcat:cbzirz6ua5bpnndsn4zs7zx5uy

Information Retrieval Features for Personality Traits

Edson Roberto Duarte Weren
2015 Conference and Labs of the Evaluation Forum  
The main goal was to test the use of features derived from Information Retrieval to identify the personality traits of the author of a given text.  ...  This paper describes the features, the classification algorithms employed, and how the experiments were run. Also, I provide a comparative analysis of my results compared to those of other groups.  ...  I thank to Viviane Pereira Moreira for their help in the final revision of this paper.  ... 
dblp:conf/clef/Weren15 fatcat:nxnxnl2jmvbzjfgcd24b2len3m

A cross-benchmark comparison of 87 learning to rank methods

Niek Tax, Sander Bockting, Djoerd Hiemstra
2015 Information Processing & Management  
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task.  ...  Our comparison methodology consists of two components: 1) Normalized Winning Number, which gives insight in the ranking accuracy of the learning to rank method, and 2) Ideal Winning Number, which gives  ...  Fast learning of document ranking functions with the committee perceptron. In Proceed- ings of the 2008 International Conference on Web Search and Data Mining (WSDM), pages 55-64.  ... 
doi:10.1016/j.ipm.2015.07.002 fatcat:vityxuoyxzfezhdizq7sfofwka

On updates that constrain the features' connections during learning

Omid Madani, Jian Huang
2008 Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08  
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial.  ...  We empirically explore the methods, and compare performance to previous indexing techniques, developed with the same goals, as well as other online algorithms based on prototype learning.  ...  Acknowledgments Many thanks to the reviewers for valuable feedback and suggestions. The authors benefited from discussions with the machine learning and data mining groups at Yahoo!  ... 
doi:10.1145/1401890.1401954 dblp:conf/kdd/MadaniH08 fatcat:wsr5kviodvhareqlycd4qguwou

Graph-based approach for airborne light detection and ranging segmentation

David L. Vilariño, José C. Cabaleiro, Jorge Martínez, Francisco F. Rivera, Tomás F. Pena
2017 Journal of Applied Remote Sensing  
A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer  ...  perceptrons implemented in R with the caret package).  ...  Acknowledgments We would like to acknowledge support from the Spanish Ministry of Science and Innovation (MICINN), which supported this work under projects TIN2011-22935 and TIN2012-32262.  ... 
doi:10.1117/1.jrs.11.015020 fatcat:6w3qqyj7vvckjkfzuulhzwojb4
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