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Owing to its simplicity and flexibility, the decision tree remains an important analysis tool in many real-world learning tasks. A lot of decision tree algorithms have been proposed, such as ID3, C4.5 and CART, which represent three most prevalent criteria of attribute splitting, i.e., Shannon entropy, Gain Ratio and Gini index respectively. These splitting criteria seem to be independent and to work...
The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. Most of them, however, are greedy algorithms that have the drawback of obtaining only local optimums. Besides, conventional split criteria they used, e.g. Shannon entropy,...
In distributed storage systems, locally repairable codes (LRCs) have attracted lots of interest recently. If a code symbol can be repaired respectively by $t$ disjoint groups of other symbols, each of which has size at most $r$, we say that the code symbol has $(r,t) $-locality. LDPC codes are linear block codes defined by low-density parity-check matrices. A regular $(\tau,\rho)$-LDPC code has the...
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