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TZID:Asia/Kolkata
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DTSTART:19451014T230000
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BEGIN:VEVENT
DTSTAMP:20160102T102603Z
UID:F222EFD7-E5B6-11E7-833E-0050568D7F66
DTSTART;TZID=Asia/Kolkata:20151218T090000
DTEND;TZID=Asia/Kolkata:20151218T143000
DESCRIPTION:Key note 2 : Uncertainty Handling using Neural Network-Based Pr
 ediction Intervals\n\nProf. Dipti Srinivasan\,\nNUS\, Singapore\n\nAbstrac
 t : Prediction Intervals (PI) are powerful tools to quantify the potential
  uncertainties associated with datasets\, and are often used in conjunctio
 n with neural networks for forecasting applications. Prediction intervals 
 are comprised of upper and lower bounds that envelope the unknown values w
 ith a probability termed as a confidence level. The primary objective for 
 constructing the PIs is to improve the prediction accuracy by quantifying 
 the uncertainties associated with point forecasts. These PIs can then be e
 fficiently used in the decision making process and for evaluation of multi
 ple scenarios that the decision makers often face in large scale practical
  problems. This talk will discuss prediction-interval based uncertainty mo
 deling methodologies for decision making and risk assessment\, and its app
 lication in a smart grid environment.\n\nKey Note 3: A Fuzzy Approach to B
 ig Data Analytics Problems\n\nDr. Raghuram Krishnapuram \,\nProgram Manage
 r\, Financial Services\, Xerox Innovation Group\, India\n\nAbstract:\n\nWi
 th the explosion of information sources\, sensor data and social media\, u
 nstructured and noisy data has become ubiquitous. In the coming years of "
 big data\," the proportion of uncertain data will only increase. The infor
 mation management and data analytics communities need to find new ways of 
 addressing this problem by devising techniques to deal with the four v's o
 f big data\, namely volume\, variety\, velocity and veracity. This talk wi
 ll describe why fuzzy techniques are more relevant now than ever before\, 
 in order to deal with this challenge. In particular\, the talk will addres
 s issues related to the "four v's" in clustering as well as other combinat
 orial optimization problems such as graph matching\, and also outline rece
 nt developments in soft representations that have made a significant diffe
 rence in the areas of natural language processing and text analytics.\n\nC
 o-sponsored by: Prof. Ravi\, IDRBT\, Hyderabad\n\nSpeaker(s): Prof. Dipti 
 Srinivasan\, Dr. Raghuram Krishnapuram\n\nHyderabad\, Andhra Pradesh\, Ind
 ia
LOCATION:Hyderabad\, Andhra Pradesh\, India
ORGANIZER:padmarav@gmail.com
SEQUENCE:0
SUMMARY:[Legacy Report] (Day 2) 5th International Conference on Fuzzy and N
 eural Computing (FANCCO 2015)
URL;VALUE=URI:https://events.vtools.ieee.org/m/125942
X-ALT-DESC:Description: <br /><p>Key note 2 :&nbsp\;Uncertainty Handling us
 ing Neural Network-Based Prediction Intervals</p>\n<p>Prof. Dipti Srinivas
 an\,<br /><strong>NUS\, Singapore</strong></p>\n<p>Abstract : Prediction I
 ntervals (PI) are powerful tools to quantify the potential uncertainties a
 ssociated with datasets\, and are often used in conjunction with neural ne
 tworks for forecasting applications. Prediction intervals are comprised of
  upper and lower bounds that envelope the unknown values with a probabilit
 y termed as a confidence level. The primary objective for constructing the
  PIs is to improve the prediction accuracy by quantifying the uncertaintie
 s associated with point forecasts. These PIs can then be efficiently used 
 in the decision making process and for evaluation of multiple scenarios th
 at the decision makers often face in large scale practical problems. This 
 talk will discuss prediction-interval based uncertainty modeling methodolo
 gies for decision making and risk assessment\, and its application in a sm
 art grid environment.</p>\n<p>&nbsp\;</p>\n<p>Key Note 3:&nbsp\;A Fuzzy Ap
 proach to Big Data Analytics Problems</p>\n<p>Dr. Raghuram Krishnapuram&nb
 sp\;\,<br /><strong>Program Manager\, Financial Services\, Xerox Innovatio
 n Group\, India</strong></p>\n<p><strong>Abstract:&nbsp\;</strong></p>\n<p
 >With the explosion of information sources\, sensor data and social media\
 , unstructured and noisy data has become ubiquitous. In the coming years o
 f "big data\," the proportion of uncertain data will only increase. The in
 formation management and data analytics communities need to find new ways 
 of addressing this problem by devising techniques to deal with the four v'
 s of big data\, namely volume\, variety\, velocity and veracity. This talk
  will describe why fuzzy techniques are more relevant now than ever before
 \, in order to deal with this challenge. In particular\, the talk will add
 ress issues related to the "four v's" in clustering as well as other combi
 natorial optimization problems such as graph matching\, and also outline r
 ecent developments in soft representations that have made a significant di
 fference in the areas of natural language processing and text analytics.</
 p>\n<p>&nbsp\;</p>
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