此篇論文考慮兩個問題：(1)在室內無線網路環境中估測節點的實體位置，(2)估測無線通訊網路的雜訊強度。在無線網路環境中，正確評估這些參數對許多工作而言是件重要的事，例如：網路管理、事件偵測、位置相關服務以及路由選擇。在此篇論文中，我們設計了 H-SVM (Hierarchical Support Vector Machine) 架構來解決以上問題。H-SVM具有以下優點：第一、因為　H-SVM　的階層式架構，它可以提供有效率的分散式計算程序。第二、　H-SVM　只需要簡單的網路資訊即可用來判斷這些參數，而不需要額外的硬體支援。第三、在　H-SVM　的架構下，估測誤差的平均值及標準差可保證在一定範圍之內。並且，我們設計了平行式學習演算法來降低計算複雜度，使在SVM的訓練資料為　n　時，計算複雜度由原本的　O(n^3)　降低為　O(n^2)。最後，模擬結果將驗證　H-SVM　的有效性。

Abstract—We consider the problems of (1)estimating the physical locations of nodes in an indoor wireless network, (2)estimating the channel noise in a MIMO wireless network, since knowing these parameters are important to many tasks of a wireless network such as network management, event detection, location-based service, and routing. A hierarchical support vector machines (H-SVM) scheme is proposed with the following advantages. First, H-SVM offers an efficient evaluation procedure in a distributed manner due to hierarchical structure. Second, H-SVM could determine these parameters based only on simpler network information, e.g., the hop counts, without require particular ranging hardware. Third, the exact mean and the variance of the estimation error introduced by HSVM
are derived which are seldom addressed in previous works. Furthermore, we present a parallel learning algorithm to reduce the computation time required for the proposed H-SVM. Thanks for the quicker matrix diagonization technique, our algorithm can reduce the traditional SVM learning complexity from O(n3) to O(n2) where n is the training sample size. Finally, the simulation results verify
the validity and effectiveness for the proposed H-SVM with parallel learning algorithm.

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