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Sequential Predictions based on Algorithmic Complexity [article]

Marcus Hutter
2005 arXiv   pre-print
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff's universal prior M,  ...  Despite this closeness to M, it is difficult to assess the prediction quality of m, since little is known about the closeness of their posteriors, which are the important quantities for prediction.  ...  The results of this work have shown that for m-based prediction one has to make extra assumptions (as compared to M).  ... 
arXiv:cs/0508043v1 fatcat:psltsxgxeba5pk6bb7af3l27fm

Sequential predictions based on algorithmic complexity

Marcus Hutter
2006 Journal of computer and system sciences (Print)  
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff's universal prior  ...  Despite this closeness to M, it is difficult to assess the prediction quality of m, since little is known about the closeness of their posteriors, which are the important quantities for prediction.  ...  The results of this work have shown that for m-based prediction one has to make extra assumptions (as compared to M).  ... 
doi:10.1016/j.jcss.2005.07.001 fatcat:tpvox6g6jbakjlfnu6ti2aw2vy

Universal Piecewise Linear Prediction Via Context Trees

Suleyman S. Kozat, Andrew C. Singer, Georg Christoph Zeitler
2007 IEEE Transactions on Signal Processing  
This performance is achieved with a prediction algorithm whose complexity is only linear in the depth of the context tree per prediction.  ...  parameters within each region of the partition, based on observing the entire sequence in advance, uniformly, for every bounded individual sequence.  ...  Using context trees and methods based on sequential probability assignment, we have shown a prediction algorithm whose total squared prediction error is within of that of the best piecewise linear model  ... 
doi:10.1109/tsp.2007.894235 fatcat:d5m74dpnxnfrroanggb56jyjk4

Universal Switching Linear Least Squares Prediction

S.S. Kozat, A.C. Singer
2008 IEEE Transactions on Signal Processing  
We construct lower bounds on the performance of any sequential algorithm, demonstrating a form of min-max optimality under certain settings.  ...  Willems, 1996) to compete with an exponential number of algorithms in the class, using complexity that is linear in the data length.  ...  We focus on these assignments since they yield sequential prediction algorithms with linear complexity in .  ... 
doi:10.1109/tsp.2007.901161 fatcat:7nmnr7xqx5ap7hxvxo6slyhmhm

Auxiliary Information-Enhanced Recommendations

Shoujin Wang, Wanggen Wan, Tong Qu, Yanqiu Dong
2021 Applied Sciences  
However, most of the existing sequential recommendation algorithms mainly focus on the sequential dependencies between item IDs within sequences, while ignoring the rich and complex relations embedded  ...  Extensive experiments on two real-world datasets demonstrate the superiority of MFN4Rec over state-of-the-art sequential recommendation algorithms.  ...  and deep-learning-based sequential recommendation algorithms.  ... 
doi:10.3390/app11198830 fatcat:5a63vnnynve3bovo436qzte3x4

Competitive Randomized Nonlinear Prediction Under Additive Noise

Y. Yilmaz, S.S. Kozat
2010 IEEE Signal Processing Letters  
We introduce a randomized algorithm based on context-trees [1].  ...  We consider sequential nonlinear prediction of a bounded, real-valued and deterministic signal from its noise-corrupted past samples in a competitive algorithm framework.  ...  To this end, we introduce a novel randomized prediction algorithm based on context-trees that uses only the past noisy samples of a desired signal and has computational complexity only linear in the depth  ... 
doi:10.1109/lsp.2009.2039950 fatcat:dwhhechq7fbyfgl3smf44625si

Efficient web usage mining process for sequential patterns

Sang T. T. Nguyen
2009 Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services - iiWAS '09  
It is designed based on the integration of the dynamic clustering-based Markov model with the Pre-Order Linked WAP-Tree Mining (PLWAP) algorithm to enhance mining performance.  ...  This process consists of three main stages: preprocessing web access sequences from the web server log, mining preprocessed web log access sequences by a tree-based algorithm, and predicting web access  ...  Helen Lu (The University of Technology, Sydney, NSW, Australia) for their advices on writing this paper.  ... 
doi:10.1145/1806338.1806425 dblp:conf/iiwas/Nguyen09 fatcat:av6f7uqrcna5vnqnegmqiji3tm

Performance Appraisal Of KDD Technique In Shopping Complex Dataset

Dr. K. Kavitha
2016 International Journal Of Engineering And Computer Science  
The basic assumption in predicting financial markets is that it is not possible. This is consistent with remarks many financial professionals have made.  ...  It predicts a value of a given continuous valued variable based on values of other variables.  ...  For example, predicting sales volumes of new product based on advertising expenditure, and time series prediction of stock market indices.  ... 
doi:10.18535/ijecs/v5i4.20 fatcat:zoy7skxuvbgwpgwyiisj5bw3oi

A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

Ra'ed M. Al-Khatib, Rosni Abdullah, Nur'Aini Abdul Rashid
2010 Evolutionary Bioinformatics  
Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods.  ...  The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods.  ...  Dirks and Pierce 32 O(n 5 ) O(n 4 ) a DP algorithm to predict base- pairing probabilities of rna with pseudoknots based on a partition function and MFE.  ... 
doi:10.4137/ebo.s4058 pmid:20458364 pmcid:PMC2865774 fatcat:rlz6c66y5nddffxq6lhlcahdu4

Gambling using a finite state machine

M. Feder
1991 IEEE Transactions on Information Theory  
Another result presented in the correspondence is a method for sequential gambling that can be based on any compression algorithm from a class of variable-to-variable length lossless compression algorithms  ...  that the exponential growth rate of the capital has the form of A specific sequential gambling scheme based on the Lempel-Ziv compression algorithm has been presented.  ... 
doi:10.1109/18.133269 fatcat:e7snccyqqfe4rh3k6p2bymeom4

A simple application of FIC to model selection [article]

Paul A. Wiggins
2015 arXiv   pre-print
We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference.  ...  The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natural emergence of model complexities with both AIC-like (N^0) and BIC-like  ...  Both AIC and BIC fail to predict the correct complexity scaling for one of the two algorithms.  ... 
arXiv:1506.06129v1 fatcat:uahss7uv7vhovcretbkti6baqm

Using Clustering Analysis and Association Rule Technology in Cross-Marketing

Yang Cheng, Ming Cheng, Tao Pang, Sizhen Liu
2021 Complexity  
In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential  ...  The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency.  ...  Data Mining Algorithm Based on Improved Sequential Pattern Mining Algorithm.  ... 
doi:10.1155/2021/9979874 doaj:2c0b001f7a37479ba7c29a5bc3adebe1 fatcat:u7lga4abdjf6ffmhn6r7t4cwia

How Well Do Teachers Predict Students' Actions in Solving an Ill-Defined Problem in STEM education: A Solution Using Sequential Pattern Mining

Yu-Cheng Norm Lien, Wen-Jong Wu, Yu-Ling Lu
2020 IEEE Access  
The first set was to make predictions based on the first known "(C)," and the second set was to make predictions based on the currently known "(C)  (F)."  ...  To fulfill this, the algorithm sought all three-series sequential patterns in the rule set starting with "(C)  (F)" (line 11), which were based on numbers 80-84 in Fig. 5 .  ... 
doi:10.1109/access.2020.3010168 fatcat:suepgd2zyzhglc5l44jq4f55oe

Universal linear prediction by model order weighting

A.C. Singer, M. Feder
1999 IEEE Transactions on Signal Processing  
We address this problem for linear prediction, but instead of fixing a specific model order, we develop a sequential prediction algorithm whose sequentially accumulated average squared prediction error  ...  Efficient lattice filters are used to generate the predictions of all the models recursively, resulting in a complexity of the universal algorithm that is no larger than that of the largest model order  ...  This algorithm is based on a prewindowed leastsquares lattice algorithm with a posteriori residuals.  ... 
doi:10.1109/78.790651 fatcat:qcrho4ujtrc5bbqr3bcrhzgkv4

A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation

G.-B. Huang, P. Saratchandran, N. Sundararajan
2005 IEEE Transactions on Neural Networks  
The growing and pruning strategy of GGAP-RBF is based on linking the required learning accuracy with the significance of the nearest or intentionally added new neuron.  ...  This paper presents a new sequential learning algorithm for radial basis function (RBF) networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF).  ...  Chaotic Time Series (Mackey-Glass) Prediction One possible application of GGAP-RBF is to predict complex time series, a special function approximation problem.  ... 
doi:10.1109/tnn.2004.836241 pmid:15732389 fatcat:zmp5ukcyenhiro2sl7ul22rgpi
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