Sharif Digital Repository

The Markov decision process is one of the most common probabilistic prediction models used in infrastructure management. When existing data are insufficient, expert knowledge is commonly used to derive a Markovian transition probability matrix. Eventually, every pavement management system will progress to a level at which inspection measurements from the network will be organized into a database to be used for performance prediction. The best way to use this body of data to improve the initially developed transition probability matrix is to combine prior expert knowledge with new observations. This paper proposes a method for periodically updating Markovian transition probabilities as new... 

In this paper, we argue that in many basic algorithms for machine learning, including support vector machine (SVM) for classification, principal component analysis (PCA) for dimensionality reduction, and regression for dependency estimation, we need the inner products of the data samples, rather than the data samples themselves.Motivated by the above observation, we introduce the problem of private inner product retrieval for distributed machine learning, where we have a system including a database of some files, duplicated across some non-colluding servers. A user intends to retrieve a subset of specific size of the set of the inner product of every pair of data items in the database with...