Clustering is an unsupervised learning method that is used to group similar objects. One of the most popular and efficient clustering methods is K-means, as it has linear time complexity and is simple to implement. However, it suffers from gets trapped in local optima. Therefore, many methods have been produced by hybridizing K-means and other methods. In this paper, we propose a hybrid method ...

In this paper, we investigate the complexity of computing locally optimal solutions for the local search problem MAXIMUM CONSTRAINT ASSIGNMENT (MCA). For our investigation, we use the framework of PLS, as defined in Johnson et al. [9]. In a nutshell, the MCA-problem is a local search version of weighted, generalized MAXSAT on constraints (functions mapping assignments to integers) over variable...

We present new results from applying the assumptions of two-player game searches, namely limited search horizon and commitment to moves in constant time, to single-agent problem-solving searches. We show that the search depth achievable with alpha pruning for a given amount of computation actually increases with increasing branching factor. We prove that real-time-A* (RTA*) makes locally optima...

The problem Minimum Convex Cover of covering a given polygon with a minimum number of (possibly overlapping) convex polygons is known to be NP -hard, even for polygons without holes [3]. We propose a polynomial-time approximation algorithm for this problem for polygons with or without holes that achieves an approximation ratio of O(logn), where n is the number of vertices in the input polygon. ...

We propose Spherical Structured Feature (SSF) maps to approximate shift and rotation invariant kernels as well as b-order arc-cosine kernels (Cho & Saul, 2009). We construct SSF maps based on the point set on d − 1 dimensional sphere Sd−1. We prove that the inner product of SSF maps are unbiased estimates for above kernels if asymptotically uniformly distributed point set on Sd−1 is given. Acco...

We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In particular, we do not assume that the robot positions are known, but estimate their positions using relative onboard sensing. Our probabilistic localization and control method takes into account the motion and sensing capabilities of the individual robots to minimize the...

This article studies a locally optimal test φ for testing H0 : θ ≥ θ0 versus H1 : θ < θ0 for the lifetime parameter θ in an exponential distribution based on type-I censored data. Certain properties associated with φ are addressed. We compare the performance of φ with that of Spurrier and Wei’s [Spurrier, J.D., Wei, L.J., 1980. A test of the parameter of the exponential distribution in the type...