Stem rare$ (all stems)

30 papers:

CHI-2015-MacLeodOGCS #towards

Rare World: Towards Technology for Rare Diseases (HM, KO, DG, KC, KAS), pp. 1145–1154.

VLDB-2015-BegumK14 #bound

Rare Time Series Motif Discovery from Unbounded Streams (NB, EJK), pp. 149–160.

STOC-2014-GoldreichW #algorithm #on the

On derandomizing algorithms that err extremely rarely (OG, AW), pp. 109–118.

CIKM-2014-PimplikarGBP #learning

Learning to Propagate Rare Labels (RP, DG, DB, GRP), pp. 201–210.

ICML-c2-2014-0001NKA #estimation #probability

GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare (AA, HN, SK, SA), pp. 1989–1997.

KDD-2014-DundarYR #identification #towards

Batch discovery of recurring rare classes toward identifying anomalous samples (MD, HZY, BR), pp. 223–232.

SAC-2014-HuWZ #documentation #internet #topic

The discovery of user related rare sequential patterns of topics in the internet document stream (ZH, HW, JZ), pp. 137–138.

CAV-2013-JegourelLS #model checking #statistics

Importance Splitting for Statistical Model Checking Rare Properties (CJ, AL, SS), pp. 576–591.

ISSTA-2013-ChocklerEY #concurrent #fault

Finding rare numerical stability errors in concurrent computations (HC, KE, EY), pp. 12–22.

KDD-2011-KotaA #multi

Temporal multi-hierarchy smoothing for estimating rates of rare events (NK, DA), pp. 1361–1369.

SIGIR-2011-JainOV #query #web

Synthesizing high utility suggestions for rare web search queries (AJ, UO, EV), pp. 805–814.

ICML-2010-SyedR #dataset #identification

Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes (ZS, IR), pp. 1023–1030.

ICPR-2010-HeG #classification

Rare Class Classification by Support Vector Machine (HH, AG), pp. 548–551.

KDD-2010-AgarwalAKK #modelling #multi #scalability

Estimating rates of rare events with multiple hierarchies through scalable log-linear models (DA, RA, RK, NK), pp. 213–222.

SIGIR-2010-DaveV #learning

Learning the click-through rate for rare/new ads from similar ads (KSD, VV), pp. 897–898.

KDIR-2009-KiranR #approach

An Improved Frequent Pattern-growth Approach to Discover Rare Association Rules (RUK, PKR), pp. 43–52.

ICML-2008-FrankMP #learning

Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.

DATE-2007-SingheeR #monte carlo #novel #performance #simulation #statistics

Statistical blockade: a novel method for very fast Monte Carlo simulation of rare circuit events, and its application (AS, RAR), pp. 1379–1384.

KDD-2007-AgarwalBCDJS #multi

Estimating rates of rare events at multiple resolutions (DA, AZB, DC, DD, VJ, MS), pp. 16–25.

KDD-2007-WuWCX #analysis #composition

Local decomposition for rare class analysis (JW, HX, PW, JC), pp. 814–823.

SIGIR-2007-BroderFGJJZ #classification #query #robust #using #web

Robust classification of rare queries using web knowledge (AZB, MF, EG, AJ, VJ, TZ), pp. 231–238.

SIGIR-2007-DowneyDH #query #web

Heads and tails: studies of web search with common and rare queries (DD, STD, EH), pp. 847–848.

DAC-2006-KanjJN #analysis #design

Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events (RK, RVJ, SRN), pp. 69–72.

KDD-2005-Petrushin #mining #multi #self #using #video

Mining rare and frequent events in multi-camera surveillance video using self-organizing maps (VAP), pp. 794–800.

KDD-2005-PhanNHH #learning

Improving discriminative sequential learning with rare--but--important associations (XHP, MLN, TBH, SH), pp. 304–313.

ICPR-v4-2004-ChanHSP #detection #semantics #using #video

Detecting Rare Events in Video Using Semantic Primitives with HMM (MTC, AH, JS, MP), pp. 150–154.

KDD-2002-JoshiAK #predict #question

Predicting rare classes: can boosting make any weak learner strong? (MVJ, RCA, VK), pp. 297–306.

AdaEurope-2000-BarrazaPCC #development #predict

An Application of the Chains-of-Rare-Events Model to Software Development Failure Prediction (NRB, JDP, BCF, FC), pp. 185–195.

KDD-1998-WeissH #learning #predict #sequence

Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.

ICML-1995-Weiss #learning

Learning with Rare Cases and Small Disjuncts (GMW), pp. 558–565.