Search


PDF Archive search engine
Last database update: 08 December at 18:10 - Around 76000 files indexed.


Show results per page

Results for «prediction»:


Total: 900 results - 0.074 seconds

Athens School 100%

Prediction Algorithms;

https://www.pdf-archive.com/2011/04/06/athens-school/

06/04/2011 www.pdf-archive.com

Probability and Cognition 99%

The hierarchical predictive processing hypothesis—also hierarchical predictive coding (Rao and Ballard, 1999), prediction error minimization (Hohwy, 2013), or action-oriented predictive processing (Clark, 2013)—says that at each level of a hierarchical brain system, predictions of what the incoming sensory data are most likely to be are encoded by populations of neurons.

https://www.pdf-archive.com/2016/09/15/probability-and-cognition/

15/09/2016 www.pdf-archive.com

P1 Instructions 98%

If the user types a number (1-5), you should consider the user to have selected the corresponding prediction, and restart the process for a new word.

https://www.pdf-archive.com/2018/01/26/p1-instructions/

26/01/2018 www.pdf-archive.com

ByteBracket 98%

an efficient method for encoding, sharing, and scoring prediction brackets for single elimination tournaments Andy Brown1∗ 1 ∗ Research and Development, Udacity To whom correspondence should be addressed;

https://www.pdf-archive.com/2016/03/17/bytebracket/

17/03/2016 www.pdf-archive.com

Sub-Optimal as Optimal 97%

Problematically, some theories propose that the brain is unified as a “prediction machine” or “inference machine,” or a “Bayesian brain.”1 The philosopher of cognitive science Andy Clark writes extensively about a “unified science of mind, brain, and action,” (2013) made possible by the theoretical hierarchical Bayesian predictive coding (PC) framework.

https://www.pdf-archive.com/2016/09/15/sub-optimal-as-optimal/

15/09/2016 www.pdf-archive.com

PosterSessionProgram 96%

Learning Depth from Image Bokeh for Robotic Perception Eric Cristofalo, Zijian Wang 157 Computer Vision Applying Machine Learning Techniques to Steering Angle Prediction in Self-Driving Cars Petar Penkov, Vinay Sriram, James Ye 158 Computer Vision ASL Fingerspelling Interpretation Shalini Ranmuthu, Ishan Patil, Hans Magnus Ewald 159 Computer Vision Automated Image-based Detection of State of Construction Progress hesam hamledari 160 Computer Vision Classification of Driver Distraction Danni Luo, Sam Colbran, Kaiqi Cen 161 Computer Vision Classification of micro-UAVs with EO Sensors Ned Danyliw, Markus Diehl 162 Computer Vision ColoRNN Book:

https://www.pdf-archive.com/2016/12/13/postersessionprogram/

13/12/2016 www.pdf-archive.com

background, theory 96%

1 Background:

https://www.pdf-archive.com/2013/05/30/background-theory/

29/05/2013 www.pdf-archive.com

CIUpdate 96%

February 13, 2018 What’s in this issue?

https://www.pdf-archive.com/2018/02/15/ciupdate/

15/02/2018 www.pdf-archive.com

main 96%

Network Selection Algorithms for Multi-Homed mobile Terminals in a Heterogeneous Network Using Utility-based MADM and Mobile Terminal Movement Prediction by Jiamo Liu Prepared for O.

https://www.pdf-archive.com/2017/02/02/main/

02/02/2017 www.pdf-archive.com

KyuCho cv 95%

of Washington • Stock Market Price Prediction • Money Ball and NBA Champion Prediction • Loan Repayment Rate Prediction • Forecasting Interest Rate by the Fed.

https://www.pdf-archive.com/2016/09/28/kyucho-cv/

28/09/2016 www.pdf-archive.com

Energy-Saving-Residential-Buildings 95%

Thus, realistic simulation and prediction of user behavior may contribute significantly in energy saving.

https://www.pdf-archive.com/2018/05/07/energy-saving-residential-buildings/

07/05/2018 www.pdf-archive.com

BB13 Predictions Post-Double Eviction 95%

10 points awarded for any correct prediction.

https://www.pdf-archive.com/2011/08/26/bb13-predictions-post-double-eviction/

26/08/2011 www.pdf-archive.com

CanIndifferenceVindicateInduction 94%

    Fool Me Once: Can Indifference Vindicate Induction?  Roger White (2015) sketches an ingenious new solution to the problem of induction. It argues on  a priori ​  grounds that the world is more likely to be induction­friendly than induction­unfriendly.  The argument relies primarily on the principle of indifference, and, somewhat surprisingly,  assumes little else. If inductive methods could be vindicated in anything like this way, it would  be quite a groundbreaking result. But there are grounds for pessimism about the envisaged  approach. This paper shows that in the crucial test cases White concentrates on, the principle of  indifference actually renders induction no more accurate than random guessing. It then diagnoses  why the indifference­based argument seems so intuitively compelling, despite being ultimately  unsound.  1 An Indifference­Based Strategy  White begins by imagining that we are “apprentice demons” tasked with devising an  induction­unfriendly world ​  – a world where inductive methods tend to be unreliable. To  simplify, we imagine that there is a single binary variable that we control (such as whether the  sun rises over a series of consecutive days). So, in essence, the task is to construct a binary  sequence such that – if the sequence were revealed one bit at a time – an inductive reasoner  would fare poorly at predicting its future bits. This task, it turns out, is surprisingly difficult. To  see this, it will be instructive to consider several possible strategies for constructing a sequence  that would frustrate an ideal inductive predictor.  Immediately, it is clear that we should avoid uniformly patterned sequences, such as:   00000000000000000000000000000000   or  01010101010101010101010101010101.  ­1­      Sequences like these are quite kind to induction. Our inductive reasoner would quickly latch onto  the obvious patterns these sequences exhibit. A more promising approach, it might seem, is to  build an apparently patternless sequence:  00101010011111000011100010010100  ​ But, importantly, while induction will not be particularly ​ ​ reliable at predicting the terms of this  sequence, it will not be particularly ​unreliable here either. Induction would simply be silent  about what a sequence like this contains. As White puts it, “ In order for... induction to be  applied, our data must contain a salient regularity of a reasonable length” (p. 285). When no  pattern whatsoever can be discerned, presumably, induction is silent. (We will assume that the  inductive predictor is permitted to suspend judgment whenever she wishes.) The original aim  was not to produce an induction­neutral sequence, but to produce a sequence that elicits errors  from induction. So an entirely patternless sequence will not suffice. Instead, the  induction­unfriendly sequence will have to be more devious, building up seeming patterns and  then violating them. As a first pass, we can try this:  00000000000000000000000000000001  Of course, this precise sequence is relatively friendly to induction. While our inductive predictor  will undoubtedly botch her prediction of the final bit, it is clear that she will be able to amass a  long string of successes prior to that point. So, on balance, the above sequence is quite kind to  induction – though not maximally so.   In order to render induction unreliable, we will need to elicit more errors than correct  predictions. We might try to achieve this as follows:  00001111000011110000111100001111  ­2­      The idea here is to offer up just enough of a pattern to warrant an inductive prediction, before  pulling the rug out – and then to repeat the same trick again and again. Of course, this precise  sequence would not necessarily be the way to render induction unreliable: For, even if we did  manage to elicit an error or two from our inductive predictor early on, it seems clear that she  would eventually catch on to the exceptionless higher­order pattern governing the behavior of  the sequence.  The upshot of these observations is not that constructing an induction­unfriendly sequence is  impossible. As White points out, constructing such a sequence should be possible, given any  complete description of how exactly induction works (p. 287). Nonetheless, even if there are a  few special sequences that can frustrate induction, it seems clear that such sequences are fairly  few and far between. In contrast, it is obviously very easy to ​corroborate induction (i.e. to  construct a sequence rendering it thoroughly reliable). So induction is relatively  un­frustrate­able. And it is worth noting that this property is fairly specific to induction. For  example, consider an inferential method based on the gambler’s fallacy, which advises one to  predict whichever outcome has occurred less often, overall. It would be quite easy to frustrate  this method thoroughly (e.g. ​00000000…​).   So far, we have identified a highly suggestive feature of induction. To put things roughly, it  can seem that:   * Over a large number of sequences, induction is thoroughly reliable.   * Over a large number of sequences, induction is silent (and hence, neither reliable nor unreliable).  * Over a very small number of sequences (i.e. those specifically designed to thwart induction),  induction is unreliable (though, even in these cases, induction is still silent much of the time).  ­3­      Viewed from this angle, it can seem reasonable to conclude that there are ​a priori grounds for  confidence that an arbitrary sequence is not induction­unfriendly. After all, there seem to be far  more induction­friendly sequences than induction­unfriendly ones. If we assign equal probability  to every possible sequence, then the probability that an arbitrary sequence will be  induction­friendly is going to be significantly higher than the probability that it will be  induction­unfriendly. So a simple appeal to the principle of indifference seems to generate the  happy verdict that induction can be expected to be more reliable than not, at least in the case of  binary sequences.   Moreover, as White points out, the general strategy is not limited to binary sequences. If we  can show ​a priori that induction over a binary sequence is unlikely to be induction­unfriendly,  then it’s plausible that a similar kind of argument can be used to show that we are justified in  assuming that an arbitrary ​world is not induction­unfriendly. If true, this would serve to fully  vindicate induction.  2 Given Indifference, Induction Is not Reliable   However, there are grounds for pessimism about whether the strategy is successful even in the  simple case of binary sequences. Suppose that, as a special promotion, a casino decided to offer  Fair Roulette. The game involves betting $1 on a particular color – black or red – and then  spinning a wheel, which is entirely half red and half black. If wrong, you lose your dollar; if  right, you get your dollar back and gain another. If it were really true that induction can be  expected to be more reliable than not over binary sequences, it would seem to follow that  induction can serve as a winning strategy, over the long term, in Fair Roulette. After all, multiple  spins of the wheel produce a binary sequence of reds and blacks. And all possible sequences are  ­4­      equally probable. Of course, induction cannot be used to win at Fair Roulette – past occurrences  of red, for example, are not evidence that the next spin is more likely to be red. This suggests that  something is amiss. Indeed, it turns out that no inferential method – whether inductive or  otherwise – can possibly be expected to be reliable at predicting unseen bits of a binary  sequence, if the principle of indifference is assumed. This can be shown as follows.  Let ​S be an unknown binary sequence of length ​n. ​S is to be revealed one bit at a time,  starting with the first.   S: ​? ? ? ? ? ? … ?​ ​:​S    n bits  Let ​f be an arbitrary predictive function that takes as input any initial subsequence of ​S and  outputs a prediction for the next bit: ‘0’, ‘1’, or ‘suspend judgment’.   A  predictive  function’s  accuracy  is measured as follows: +1 for each correct prediction; ­1 for  each  incorrect  prediction;  0  each  time ‘suspend judgment’ occurs. (So the maximum accuracy of  a  function  is  ​n;  the  minimum  score  is  –​n.)  Given  a  probability  distribution  over  all  possible  sequences,  the  ​expected  accuracy  of  a  predictive  function  is  the  average  of  its  possible  scores  weighted by their respective probabilities.  Claim: ​If we assume indifference (i.e. if we assign equal probability to every possible sequence), then  – no matter what ​S is – each of​ f’s predictions​ will be expected to contribute 0 to ​f’s accuracy. And, as  a consequence of this, ​f has 0 expected accuracy more generally.  Proof: ​For some initial subsequences, ​f will output ‘suspend judgment’. The contribution of such  predictions will inevitably be 0. So we need consider only those cases where ​f makes a firm  prediction (i.e. ‘0’ or ‘1’; not ‘suspend judgment’).  Let ​K be a ​k­length initial subsequence for which ​f makes a firm prediction about the bit in   ­5­ 

https://www.pdf-archive.com/2017/02/19/canindifferencevindicateinduction/

19/02/2017 www.pdf-archive.com

p2 1-1 94%

www.ijcncs.org ISSN 2308-9830 N C S Enhancing the Performance of DSR Routing Protocol Using Link Breakage Prediction in Vehicular Ad Hoc Network Khalid Zahedi1, Yasser Zahedi2, Abd Samad Ismail3 1 Ph.D student, Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia 2 Ph.D student, Wireless Communication Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia 3 Professor, Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia E-mail:

https://www.pdf-archive.com/2017/03/21/p2-1-1/

21/03/2017 www.pdf-archive.com

FoolMeOnce 94%

While our inductive predictor will undoubtedly botch her prediction of the final bit, it is clear that she will be able to amass a long string of successes prior to that point.

https://www.pdf-archive.com/2017/02/19/foolmeonce/

19/02/2017 www.pdf-archive.com

report 94%

8 4.1.5 Data integration for prediction locations .

https://www.pdf-archive.com/2016/06/06/report/

06/06/2016 www.pdf-archive.com

pages 93%

Our key prediction for 2017 is that alternatives will outperform commercial property.

https://www.pdf-archive.com/2017/06/08/pages/

08/06/2017 www.pdf-archive.com

poster 93%

Comparison of prediction mean and training mean Revenue Revenue measures the amount of money spent, while Engagement measures the amount of time spent in game.

https://www.pdf-archive.com/2016/10/18/poster/

18/10/2016 www.pdf-archive.com

m140002 93%

miRNA target prediction using bioinformatics tools is often the first line approach in studying gene regulation.

https://www.pdf-archive.com/2015/07/27/m140002/

27/07/2015 www.pdf-archive.com

Portfolio CR 93%

Churners profiling and prediction of customer churn probability.

https://www.pdf-archive.com/2017/07/25/portfolio-cr/

24/07/2017 www.pdf-archive.com

Final Report 92%

Accurate analysis and prediction of weather and climate is exceptionally challenging due to the higher order and often complex interactions between the many erratic variables that influence everyday climate.

https://www.pdf-archive.com/2016/12/27/final-report/

27/12/2016 www.pdf-archive.com

Football Betting Tips App 92%

AI and machine learning will increase the accuracy of the prediction.

https://www.pdf-archive.com/2018/05/11/footballbettingtipsapp-/

11/05/2018 www.pdf-archive.com

Bostrom 92%

Predictions about future technical and social developments are notoriously unreliable – to an extent that have lead some to propose that we do away with prediction altogether in our planning and preparation for the future.

https://www.pdf-archive.com/2017/06/23/bostrom/

23/06/2017 www.pdf-archive.com

Poster 91%

STRTEAMGAGE PREDICTION IN THE NORTHEAST ABSTRACT STREAMGAGES ARE TOOLS THAT MEASURES THE AMOUNT OF WATER MOVING THROUGH A RIVER OR A STREAM.

https://www.pdf-archive.com/2017/10/10/poster/

10/10/2017 www.pdf-archive.com