In this report, we studied different complex classification models such as Gradient Descent, Multiclass Classification. We also used different Machine Learning tools such as LibSVM, MEGAm, and FastDT to design a complex classifier based on OVA and AVA approaches. We also designed 2 different Rank classifier using MEGAm library and evaluated its performance on the OHSUMED database. The binary classification accuracy (0-1) error using 20 different queries and 10 retrieved documents for each query was 33% for Ranking Classifier 1. The binary classification accuracy for Ranking Classifier 2 was 37%. However, the average ranking performance, as evaluated using DCG metric, was roughly 8% better for Ranking Classifier 2 as compared with Ranking Classifier 1. This improvement comes from the cost function used to penalize the mis-ranking.

Many sensor network related applications require precise knowledge of the location of constituent nodes. In these applications,it is desirable for the wireless nodes to be able to autonomously determine their locations before they start sensing and transmitting data. Most existing localization algorithms rely on anchor nodes whose locations are known to determine the positions of the remaining nodes using methods such as triangulation or trilateration. In this work, we consider the scenario where anchor nodes are not equipped with any self-positioning functionality, and do not have the knowledge about their positions. In such cases, anchor nodes respond to a "HELLO" signal transmitted by the localizing node. The response to "HELLO" signal can be used by the localizing node to estimate the time of arrival (ToA) or Received Signal Strength Indicator (RSSI). We use such ToA or RSSI measurements to learn node locations using Support Vector Machines (SVM). We cast the problem into a regression and a multiclass classification setting, and demonstrate the high localization accuracy achieve by this approach as compared with the traditional Least Squares based solution. We also demonstrate that strategically choosing the evaluation order o...