Optimal decision trees for local image processing algorithms
نویسندگان
چکیده
منابع مشابه
Optimal decision trees for local image processing algorithms
In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connec...
متن کاملApproximation Algorithms for Optimal Decision Trees and Adaptive TSP Problems
We consider the problem of constructing optimal decision trees: given a collection of tests which can disambiguate between a set of m possible diseases, each test having a cost, and the a-priori likelihood of the patient having any particular disease, what is a good adaptive strategy to perform these tests to minimize the expected cost to identify the disease? We settle the approximability of t...
متن کاملTechnical Note: Algorithms for Optimal Dyadic Decision Trees
A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, replacing the dynamic programming alg...
متن کاملImage Processing and Image Mining using Decision Trees
Valuable information can be hidden in images, however, few research discuss data mining on them. In this paper, we propose a general framework based on the decision tree for mining and processing image data. Pixel-wised image features were extracted and transformed into a database-like table which allows various data mining algorithms to make explorations on it. Each tuple of the transformed ta...
متن کاملOptimal Decision Trees
We propose an Extreme Point Tabu Search (EPTS) algorithm that constructs globally optimal decision trees for classiication problems. Typically, decision tree algorithms are greedy. They optimize the misclassiication error of each decision sequentially. Our non-greedy approach minimizes the misclassiication error of all the decisions in the tree concurrently. Using Global Tree Optimization (GTO)...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2012
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2012.08.015