Возраст домена | 19 лет |
Дата окончания | Истек срок регистрации |
PR | 4 |
ИКС | |
Страниц в Google | 138 |
Страниц в Яндексе | 23 |
Dmoz | Нет |
Яндекс Каталог | Нет |
Alexa Traffic Rank | 17915154 |
Alexa Country | Нет данных |
История изменения показателей | Авторизация |
Идет сбор информации... Обновить
Sathiya Keerthi's Homepage
n/a
n/a
WINDOWS-1252
112.69 КБ
2 500
75 851 симв.
64 449 симв.
Данные предоставлены сервисом semrush
Сайт | Общие фразы | PR | тИЦ | Alexa Rank | Alexa Country | |
---|---|---|---|---|---|---|
![]() ![]() |
14 | 9 |
0 | 578 | ![]() | |
![]() ![]() |
12 | 9 |
0 | 5 | ![]() | |
![]() ![]() |
8 | 9 |
0 | 415 | ![]() | |
![]() ![]() |
7 | 7 |
0 | 47 | ![]() | |
![]() ![]() |
7 | 8 |
0 | 1638 | ![]() | |
![]() ![]() |
6 | 8 |
300 | 3835 | ![]() | |
![]() ![]() |
5 | 8 |
0 | 35 | ![]() | |
![]() ![]() |
5 | 8 |
0 | 1205 | ![]() | |
![]() ![]() |
4 | 8 |
0 | 1791 | ![]() | |
![]() ![]() |
4 | 6 |
0 | 5365 | ![]() | |
Еще 40 сайтов после авторизации |
Данные предоставлены сервисом semrush
Данные linkpad ( 2 Марта 2013 ) | |
Количество ссылок на сайт | 0 |
Количество доменов, которые ссылаются на сайт | 0 |
Количество найденных анкоров | 0 |
Исходящие (внешние) ссылки домена | 28 |
Количество доменов, на которые ссылается сайт | 28 |
Количество исходящих анкоров | 28 |
Внешние ссылки главной страницы ( 48 ) | |
research.criteo.com/ | Criteo Research |
linkedin.com/in/suju-rajan-9b56581/ | Suju Rajan |
research.yahoo.com/ | Yahoo! Research |
research.criteo.com/announcing-3rd-annual-machine-learning-r... | 3rd annual Machine Learning in the Real World Workshop |
eecs.umich.edu/~grizzle/GilbertFest/ | symposium page |
eecs.umich.edu/~grizzle/GilbertFest/GilbertClassics.html | pdfs of his classic papers in Control and Optimization |
eecs.umich.edu/eecs/about/articles/2017/elmer-gilbert-a-life... | A Life in Control |
csie.ntu.edu.tw/~cjlin/liblinear/ | - |
csie.ntu.edu.tw/~cjlin/ | Chih-Jen Lin |
csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/ | Distributed version |
scholar.google.com/citations?user=Sr7jln4AAAAJ&hl=en | Citations of my papers in Google Scholar |
arxiv.org/pdf/1704.06731.pdf | Batch-Expansion Training: An Efficient Optimization Framework |
aistats.org/ | AISTATS, 2018 |
arxiv.org/pdf/1511.02024v1.pdf | Towards a Better Understanding of Predict and Count Models |
cs.cornell.edu/~schnabts/ | Tobias Schnabel |
rjvak7.github.io/ | Rajiv Khanna |
ima.umn.edu/videos/?id=1551 | Large scale information extraction from the web |
olivier.chapelle.cc/pub/ordinal.pdf | Efficient algorithms for ranking with SVMs |
csie.ntu.edu.tw/~cjlin/papers/cddual.pdf | A dual coordinate descent method for large-scale linear SVM |
csie.ntu.edu.tw/~cjlin/papers/sdm_kdd.pdf | A sequential dual coordinate method for large-scale multi-class linear SVMs |
jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pd... | Optimization techniques for semi-supervised SVMs |
olivier.chapelle.cc/pub/jsm_fsel.pdf | Multi-Class Feature Selection with Support Vector Machines |
csulb.edu/journals/jecr/issues/20081/Paper2.pdf | Ad delivery with budgeted advertisers: A comprehensive LP approach |
jmlr.csail.mit.edu/papers/volume9/lin08b/lin08b.pdf | Trust-region Newton method for large scale logistic regression |
largescale.first.fraunhofer.de/media/slides/chapelle_keerthi... | Participation (two winning entries in the wild track) in PASCAL Large Scale Learning Challenge |
largescale.first.fraunhofer.de/about/ | PASCAL challenge |
machinelearning.org/proceedings/icml2007/papers/206.pdf | recent comparison of various algorithms for sequence labeling by Nguyen and Guo |
csie.ntu.edu.tw/~cjlin/papers/logistic.pdf | Trust-region Newton method for large scale logistic regression |
jmlr.csail.mit.edu/papers/volume7/keerthi06a/keerthi06a.pdf | Building Support Vector Machines with Reduced Classifier Complexity |
gatsby.ucl.ac.uk/~chuwei/ | Wei Chu |
people.cs.uchicago.edu/~vikass/svmlin.html | SVM lin |
people.cs.uchicago.edu/~vikass/ | Vikas Sindhwani |
jmlr.csail.mit.edu/papers/volume6/keerthi05a/keerthi05a.pdf | A modified finite Newton method for fast solution of large scale linear SVMs |
acm.org/sigs/sigkdd/explorations/issues/4-2-2002-12/shi.pdf | click here |
dti.nus.edu.sg/ | DTI Institute |
gis.nus.edu.sg/ | Genome Institute of Singapore |
biostat.wisc.edu/~craven/kddcup/ | KDD Cup 2002 |
newshub.nus.edu.sg/ke/0206/articles/kdd.htm | Click here for a short article written for the university monthly magazine. |
kiku.csce.kyushu-u.ac.jp/~norikazu/paper.en.html | Takahashi and Nishi |
jair.org/papers/paper734.html | JAIR 2001 |
research.microsoft.com/~jplatt/ | Platt's |
research.microsoft.com/~jplatt/smo.html | SMO |
csie.ntu.edu.tw/~cjlin/libsvm/index.html | LIBSVM |
web.eecs.umich.edu/~grizzle/GilbertFest/Gilbert%2858%29.pdf | A fast procedure for computing the distance between complex objects in 3-dimensional space |
en.wikipedia.org/wiki/Gilbert%E2%80%93Johnson%E2%80%93Keerth... | The GJK Algorithm |
mollyrocket.com/849 | video |
web.eecs.umich.edu/~grizzle/GilbertFest/%2857%29.pdf | Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: stability and moving-horizon ap... |
elsevierscitech.com/pdfs/sdarticle.pdf | this survey paper |
Внутренние ссылки главной страницы ( 66 ) | |
keerthis.com/Optimization_and_Generalization_Keerthi_Criteo_... | Optimization_and_Generalization_Keerthi_Criteo_November_08_2017.pptx |
keerthis.com/Keerthi_Optimization_For_ML_UCSC_2017.pdf | Keerthi_Optimization_For_ML_UCSC_2017.pdf |
keerthis.com/#Prior | 1999 and Earlier |
keerthis.com/jmlrex2.pdf | An efficient distributed learning algorithm based on effective local functional approximations |
keerthis.com/jmlrfp2.pdf | A distributed block coordinate descent method for training l_1 regularized linear classifiers |
keerthis.com/tobeadded.pdf | Deterministic annealing for semi-supervisedstructured output learning |
keerthis.com/ecml_dual_transduction.pdf | Tractable semi-supervised learning of complex structured prediction models |
keerthis.com/transp.pdf | Extension of TSVM to multi-class and hierarchical textclassification problems with general losses (Long version) |
keerthis.com/P12-1064.pdf | Iterative Viterbi A* algorithm for K-best sequential coding |
keerthis.com/sp158-long.pdf | A simple approach to the design of site-level extractors usingdomain-centric principles |
keerthis.com/SDM12.pdf | Regularized structured output learning with partial labels |
keerthis.com/SDM11.pdf | A sequential dual method for structural SVMs |
keerthis.com/cikm116-dhillon.pdf | Semi-supervised multi-task learning of structuredprediction models for web information extraction |
keerthis.com/cikm115-sellamanickam.pdf | A pairwise ranking based approach to learning withpositive and unlabeled examples |
keerthis.com/cikm569-selvaraj.pdf | Semi-supervised SVMs for classification with unknownclass proportions and a small labeled dataset |
keerthis.com/MLG2011_TCMMG_FINAL.pdf | Transductive classification methods for mixed graphs |
keerthis.com/Gbc_OnlineVersion.pdf | Graph based classification methods using inaccurate externalclassifier information |
keerthis.com/cvgpcv16.pdf | Predictive approaches for Gaussian process classifiermodel selection |
keerthis.com/crf_comparison_keerthi_07.pdf | CRF versus SVM-struct for sequence labeling |
keerthis.com/sund_predGP_report.pdf | Predictive approaches for sparse Gaussian process regression |
ToBeInserted.pdf | Constructing a maximum utility slate of on-line advertisements |
keerthis.com/ssgp_ijcai_sindhwani_07.pdf | Semi-supervised Gaussian processes |
keerthis.com/hyp_nips_keerthi_06.pdf | An efficient method for gradient-based adaptation of hyperparameters in SVM models |
keerthis.com/report_hypergrad.pdf | Longer tech report. |
keerthis.com/rgp_nips_chu_06.pdf | Relational learning with Gaussian processes |
keerthis.com/bb_nips_chapelle_06.pdf | Branch and bound for semi-supervised support vector machines |
keerthis.com/ord_nc_chu_06.pdf | Support vector ordinal regression |
keerthis.com/gcv_nc_sundararajan_06.pdf | Fast generalized cross validation algorithm for sparse model learning |
keerthis.com/semisup_sigir_sindhwani_06.pdf | Large-scale semi-supervised linear SVMs |
keerthis.com/semisup_techreport_06.ps | Longer tech report with pseudocodes. |
keerthis.com/newton_lskm.pdf | Newton methods for fast solution of semi-supervised linear SVMs |
keerthis.com/danneal_icml_sindhwani_06.pdf | Deterministic annealing for semi-supervised kernel machines |
parallel_SMO_IEEE.pdf | Parallel sequential minimal optimization for the training of support vector machines |
keerthis.com/glars_ieee_keerthi_06.pdf | A fast tracking algorithm for generalized LARS/LASSO |
keerthis.com/gpsparse_nips_keerthi_chu_05.ps | A matching pursuit approach to sparse Gaussian process regression |
keerthis.com/kerlogsmo_ml_keerthi_06.ps | A Fast Dual Algorithm for Kernel Logistic Regression |
keerthis.com/lssvmcg_ieee_chu_05.pdf | An improved conjugate gradient scheme to the solution of least squares SVM |
keerthis.com/ord_icml_chu_05.pdf | New approaches to support vector ordinal regression |
keerthis.com/lars_icml_keerthi_05.pdf | Generalized LARS as an effective feature selection tool for text classification with SVMs |
keerthis.com/multiclass_mcs_kaibo_05.pdf | Which is the best multiclass SVM method? An empirical study |
keerthis.com/bayessvmreg_ieee_chu_04.pdf | Bayesian support vector regression using a unified loss function |
keerthis.com/a.pdf | Rule prepending and post-pruning approach to incremental learning of decision lists |
keerthis.com/SparseLOGREG.html | A simple and efficient algorithm for gene selection using sparse logistic regression |
keerthis.com/asymprbfsvm_nc_keerthi_03.ps | Asymptotic behaviours of support vector machines with gaussian kernel |
keerthis.com/mcs_mcs_duan_03.ps | Multi-category classification by soft-max combination of binary classifiers |
keerthis.com/trig_nc_chu_03.pdf | Bayesian Trigonometric support vector classifier |
keerthis.com/btsvc/bisvm.zip | bisvm.zip |
keerthis.com/lssvmsmo_nc_keerthi_03.ps | SMO Algorithm for Least Squares SVM Formulations |
keerthis.com/comparison.html | comparison.html |
keerthis.com/radmar_ieee_keerthi_02.ps | Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms |
keerthis.com/nparm.html | nparm.html |
keerthis.com/smoconv_ml_keerthi_02.ps | Convergence of a generalized SMO algorithm for SVM classifier design |
keerthis.com/svmregbayes_ieee_chu_02.pdf | A unified loss function in Bayesian framework for support vector regression |
keerthis.com/kaibo_ncpaper.pdf | Evaluation of simple performance measures for tuning SVM hyperparameters |
keerthis.com/mf_jair_bhattacharyya_01.pdf | Mean-field methods for a special class of belief networks |
keerthis.com/smo_nc_keerthi_01.ps | Improvements to Platt's SMO algorithm for SVM classifier design |
keerthis.com/smo_mod.ps | Click here |
keerthis.com/predgp_nc_sundararajan_01.ps | Predictive approaches for choosing hyperparameters in Gaussian processes |
keerthis.com/mf_nips_bhattacharyya_00.ps | A variational mean-field theory for sigmoidal belief networks |
keerthis.com/svmnpa_ieee_keerthi_00.pdf | A fast iterative nearest point algorithm for support vector machine classifier design |
keerthis.com/npa_tr.ps | Click here |
keerthis.com/npa.tar.gz | Click here |
keerthis.com/clustering_ieeesmc_pbabu_00.pdf | A stochastic connectionist approach for global optimization with application to pattern clustering |
keerthis.com/smoreg_ieee_shevade_00.pdf | Improvements to the SMO algorithm for SVM regression |
keerthis.com/plefka_jphysics_bhattacharyya_00.ps | Information geometry and Plefka's mean-field theory |
keerthis.com/murthy_diver.pdf | Optimal control of a somersaulting platform diver: a numerical approach |
Domain Name: KEERTHIS.COM
Registry Domain ID: 134169148_DOMAIN_COM-VRSN
Registrar WHOIS Server: whois.godaddy.com
Registrar URL: http://www.godaddy.com
Updated Date: 2015-04-14T18:25:11Z
Creation Date: 2004-11-02T17:08:26Z
Registry Expiry Date: 2019-11-02T16:08:26Z
Registrar: GoDaddy.com, LLC
Registrar IANA ID: 146
Registrar Abuse Contact Email: abuse@godaddy.com
Registrar Abuse Contact Phone: 480-624-2505
Domain Status: clientDeleteProhibited https://icann.org/epp#clientDeleteProhibited
Domain Status: clientRenewProhibited https://icann.org/epp#clientRenewProhibited
Domain Status: clientTransferProhibited https://icann.org/epp#clientTransferProhibited
Domain Status: clientUpdateProhibited https://icann.org/epp#clientUpdateProhibited
Name Server: NS507A.HOSTPC.COM
Name Server: NS507B.HOSTPC.COM
URL of the ICANN Whois Inaccuracy Complaint Form: https://www.icann.org/wicf/
>>> Last update of whois database: 2018-12-07T07:00:57Z <<<
For more information on Whois status codes, please visit https://icann.org/epp
NOTICE: The expiration date displayed in this record is the date the
registrar's sponsorship of the domain name registration in the registry is
currently set to expire. This date does not necessarily reflect the expiration
date of the domain name registrant's agreement with the sponsoring
registrar. Users may consult the sponsoring registrar's Whois database to
view the registrar's reported date of expiration for this registration.
TERMS OF USE: You are not authorized to access or query our Whois
database through the use of electronic processes that are high-volume and
automated except as reasonably necessary to register domain names or
modify existing registrations; the Data in VeriSign Global Registry
Services' ("VeriSign") Whois database is provided by VeriSign for
information purposes only, and to assist persons in obtaining information
about or related to a domain name registration record. VeriSign does not
guarantee its accuracy. By submitting a Whois query, you agree to abide
by the following terms of use: You agree that you may use this Data only
for lawful purposes and that under no circumstances will you use this Data
to: (1) allow, enable, or otherwise support the transmission of mass
unsolicited, commercial advertising or solicitations via e-mail, telephone,
or facsimile; or (2) enable high volume, automated, electronic processes
that apply to VeriSign (or its computer systems). The compilation,
repackaging, dissemination or other use of this Data is expressly
prohibited without the prior written consent of VeriSign. You agree not to
use electronic processes that are automated and high-volume to access or
query the Whois database except as reasonably necessary to register
domain names or modify existing registrations. VeriSign reserves the right
to restrict your access to the Whois database in its sole discretion to ensure
operational stability. VeriSign may restrict or terminate your access to the
Whois database for failure to abide by these terms of use. VeriSign
reserves the right to modify these terms at any time.
The Registry database contains ONLY .COM, .NET, .EDU domains and
Registrars.
Индия - 144.16.140.26
Education and Research Network
ERNET India
HTTP/1.1 200 OK
Date: Thu, 20 Jun 2019 14:14:23 GMT
Server: Apache/2
Last-Modified: Sun, 31 Dec 2017 16:53:03 GMT
ETag: "1c2be-561a5b2ee19c0"
Accept-Ranges: bytes
Content-Length: 115390
Vary: Accept-Encoding,User-Agent
Content-Type: text/html
Кнопка для анализа сайта в один клик, для установки перетащите ссылку на "Панель закладок"