Automatically inferred markov network models for classification of chromosomal band pattern structures

analysis of ruin probability for insurance companies using markov chain

در این پایان نامه نشان داده ایم که چگونه می توان مدل ریسک بیمه ای اسپیرر اندرسون را به کمک زنجیره های مارکوف تعریف کرد. سپس به کمک روش های آنالیز ماتریسی احتمال برشکستگی ، میزان مازاد در هنگام برشکستگی و میزان کسری بودجه در زمان وقوع برشکستگی را محاسبه کرده ایم. هدف ما در این پایان نامه بسیار محاسباتی و کاربردی تر از روش های است که در گذشته برای محاسبه این احتمال ارائه شده است. در ابتدا ما نشا...

Timbre-based Drum Pattern Classification using Hidden Markov Models

In order to explore the possibility of a timbre-based rhythm theory, a drum pattern classification system was developed, which is capable of describing the internal structure of a drum groove in a stochastic way. Using an onset detection algorithm, timbral features were extracted at every drum onset of the sample file. Next, a Hidden Markov Model (HMM) was fitted to the data. Local decoding of ...

Markov Models for Classification of Protein Helices

investigating the feasibility of a proposed model for geometric design of deployable arch structures

deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...

Externally Growing Cell Structures for Pattern Classification

Based on B. Fritzke’s GCS (Growing Cell Structures) we present here a new incremental self-organizing neural network, the Externally Growing Cell Structures (EGCS). Our goals are to speed up the convergence and to improve the generalization performance. The mechanism of internally growing cells in EGCS is the same as in GCS. However, when the Maximum Resource Vertex (MRV) or the Maximum Error V...