Stem naiv$ (all stems)

55 papers:

ICML-2015-Betancourt #monte carlo #scalability

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling (MB), pp. 533–540.

ICPR-2014-FornoniC #learning #naive bayes #recognition

Scene Recognition with Naive Bayes Non-linear Learning (MF, BC), pp. 3404–3409.

PODS-2013-GheerbrantLS #evaluation #question

When is naive evaluation possible? (AG, LL, CS), pp. 75–86.

CIKM-2012-ChenW #automation #classification #naive bayes

Automated feature weighting in naive bayes for high-dimensional data classification (LC, SW), pp. 1243–1252.

SIGIR-2012-NunzioS #classification #data analysis #naive bayes #visual notation

A visual tool for bayesian data analysis: the impact of smoothing on naive bayes text classifiers (GMDN, AS), p. 1002.

CIKM-2011-TomasevRMI #approach #classification #nearest neighbour #probability

A probabilistic approach to nearest-neighbor classification: naive hubness bayesian kNN (NT, MR, DM, MI), pp. 2173–2176.

ICML-2011-SuSM #classification #multi #naive bayes #scalability #using

Large Scale Text Classification using Semisupervised Multinomial Naive Bayes (JS, JSS, SM), pp. 97–104.

KDIR-2011-MohammadzadehGSN #documentation #web

Extracting the Main Content of Web Documents based on a Naive Smoothing Method (HM, TG, FS, GN), pp. 470–475.

ECSA-2010-HeeschA #architecture #bibliography #comprehension #process #reasoning #student

Naive Architecting — Understanding the Reasoning Process of Students — A Descriptive Survey (UvH, PA), pp. 24–37.

FLOPS-2010-SeidelV #automation #generative #theorem

Automatically Generating Counterexamples to Naive Free Theorems (DS, JV), pp. 175–190.

ICPR-2010-FuLTZ #classification #learning #music #naive bayes #retrieval

Learning Naive Bayes Classifiers for Music Classification and Retrieval (ZF, GL, KMT, DZ), pp. 4589–4592.

ICPR-2010-GodecLSB #naive bayes #online #random

On-Line Random Naive Bayes for Tracking (MG, CL, AS, HB), pp. 3545–3548.

SIGIR-2010-HagenPSB #power of #query #segmentation

The power of naive query segmentation (MH, MP, BS, CB), pp. 797–798.

VLDB-2009-MozafariZ #classification #naive bayes #privacy

Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss (BM, CZ), pp. 1173–1185.

ECIR-2009-TanCWX #adaptation #analysis #naive bayes #sentiment

Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis (ST, XC, YW, HX), pp. 337–349.

SIGIR-2008-HueteCFR #modelling #naive bayes #representation

Hierarchical naive bayes models for representing user profiles (JFH, LMdC, JMFL, MARM), pp. 711–712.

ECIR-2007-HeD #classification #naive bayes #using

Improving Naive Bayes Text Classifier Using Smoothing Methods (FH, XD), pp. 703–707.

RecSys-2007-PronkVPT #classification #naive bayes #recommendation

Incorporating user control into recommender systems based on naive bayesian classification (VP, WFJV, AP, MT), pp. 73–80.

PADL-2007-MorimotoTI #performance

Instantly Turning a Naive Exhaustive Search into Three Efficient Searches with Pruning (TM, YT, HI), pp. 65–79.

SAC-2007-JinLSB #automation #categorisation #naive bayes #web

Automatic web pages categorization with ReliefF and Hidden Naive Bayes (XJ, RL, XS, RB), pp. 617–621.

ECIR-2006-YinP #adaptation #classification #naive bayes #rank

Adapting the Naive Bayes Classifier to Rank Procedural Texts (LY, RP), pp. 179–190.

ICML-2006-DenisMR #classification #learning #naive bayes #performance

Efficient learning of Naive Bayes classifiers under class-conditional classification noise (FD, CNM, LR), pp. 265–272.

ICPR-v3-2006-BeveridgeSR #classification #comparison #detection #naive bayes #using

A Comparison of Pixel, Edge andWavelet Features for Face Detection using a Semi-Naive Bayesian Classifier (JRB, JS, BR), pp. 1175–1178.

ICPR-v3-2006-Martinez-ArroyoS #classification #learning #naive bayes

Learning an Optimal Naive Bayes Classifier (MMA, LES), pp. 1236–1239.

ICPR-v4-2006-Martinez-ArroyoS06a #classification #learning #naive bayes

Learning an Optimal Naive Bayes Classifier (MMA, LES), p. 958.

ICML-2005-JingPR #classification #learning #naive bayes #network #performance

Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes (YJ, VP, JMR), pp. 369–376.

ICML-2005-LowdD #estimation #modelling #naive bayes #probability

Naive Bayes models for probability estimation (DL, PMD), pp. 529–536.

ICML-2005-ZhangJS #naive bayes #ranking

Augmenting naive Bayes for ranking (HZ, LJ, JS), pp. 1020–1027.

KDD-2005-Kolcz #classification #naive bayes

Local sparsity control for naive Bayes with extreme misclassification costs (AK), pp. 128–137.

SAC-2005-HanXZG #ambiguity #naive bayes

A hierarchical naive Bayes mixture model for name disambiguation in author citations (HH, WX, HZ, CLG), pp. 1065–1069.

ICPR-v3-2004-SotocaSP #multi #naive bayes #set #using

Attribute Relevance in Multiclass Data Sets Using the Naive Bayes Rule (JMS, JSS, FP), pp. 426–429.

KDD-2004-PavlovBDKP #classification #clustering #documentation #multi #naive bayes #preprocessor

Document preprocessing for naive Bayes classification and clustering with mixture of multinomials (DP, RB, BD, SK, JP), pp. 829–834.

PPDP-2004-ZhouSS #evaluation #linear

Semi-naive evaluation in linear tabling (NFZ, YDS, TS), pp. 90–97.

SAC-2004-AmorBE #detection #naive bayes

Naive Bayes vs decision trees in intrusion detection systems (NBA, SB, ZE), pp. 420–424.

ECIR-2003-PengS #classification #modelling #n-gram #naive bayes

Combining Naive Bayes and n-Gram Language Models for Text Classification (FP, DS), pp. 335–350.

ICML-2003-CerquidesM #learning #modelling #naive bayes

Tractable Bayesian Learning of Tree Augmented Naive Bayes Models (JC, RLdM), pp. 75–82.

ICML-2003-RennieSTK #classification #naive bayes

Tackling the Poor Assumptions of Naive Bayes Text Classifiers (JDR, LS, JT, DRK), pp. 616–623.

ICML-2002-DashC #classification #naive bayes

Exact model averaging with naive Bayesian classifiers (DD, GFC), pp. 91–98.

ICML-2002-YangW #classification

Non-Disjoint Discretization for Naive-Bayes Classifiers (YY, GIW), pp. 666–673.

ICPR-v1-2002-SebeLCGH #classification #naive bayes #recognition #using

Emotion Recognition Using a Cauchy Naive Bayes Classifier (NS, MSL, IC, AG, TSH), p. 17–?.

ICPR-v2-2002-Keren #identification #naive bayes #using

Painter Identification Using Local Features and Naive Bayes (DK), pp. 474–477.

SIGIR-2002-KimRL #classification #estimation #multi #naive bayes #parametricity

A new method of parameter estimation for multinomial naive bayes text classifiers (SBK, HCR, HSL), pp. 391–392.

ICML-2001-ZadroznyE #classification #naive bayes #probability

Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (BZ, CE), pp. 609–616.

ICML-2001-ZhangL #naive bayes

Learnability of Augmented Naive Bayes in Nonimal Domains (HZ, CXL), pp. 617–623.

ICML-2000-HsuHW #classification #naive bayes #why

Why Discretization Works for Naive Bayesian Classifiers (CNH, HJH, TTW), pp. 399–406.

SIGIR-2000-AndroutsopoulosKCS #anti #comparison #email #keyword #naive bayes

An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages (IA, JK, KC, CDS), pp. 160–167.

SIGIR-2000-KimHZ #classification #naive bayes

Text filtering by boosting naive bayes classifiers (YHK, SYH, BTZ), pp. 168–175.

ICML-1999-LangleyS #analysis #classification #naive bayes

Tractable Average-Case Analysis of Naive Bayesian Classifiers (PL, SS), pp. 220–228.

ICML-1999-MladenicG #feature model #naive bayes

Feature Selection for Unbalanced Class Distribution and Naive Bayes (DM, MG), pp. 258–267.

ICML-1999-ZhengWT #lazy evaluation #learning #naive bayes

Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ZZ, GIW, KMT), pp. 493–502.

HCI-SEC-1997-BianchiBMRS #design #interface #modelling

Participatory Interface Design: From Naive Models to Systems (NB, PB, PM, GR, MGS), pp. 573–576.

KDD-1996-Kohavi #classification #hybrid #scalability

Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid (RK), pp. 202–207.

JICSLP-1996-PettorossiPR #algorithm #deduction #how #specification #string

How to Extend Partial Deduction to Derive the KMP String-Matching Algorithm from a Naive Specification (Poster Abstract) (AP, MP, SR), p. 539.

VDME-1990-TarleckiW

A Naive Domain Universe for VDM (AT, MW), pp. 552–579.

ICLP-1986-MahlerSS86 #approach #prolog

A New Approach for Intruducing Prolog to Naive Users (OM, ZS, EYS), pp. 544–551.