Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable. This paper describes a method for training CRFs on such tasks, using erro...

Automobile sector forms the backbone of manufacturing sector. Vehicle assembly line is important section in automobile plant where repetitive tasks are performed one after another at different workstations. In this thesis, a methodology is proposed to reduce cycle time and time loss due to important factors like equipment failure, shortage of inventory, absenteeism, set-up, material handling, r...

Kolmogorov complexity and algorithmic probability quantify the randomness and universal a priori probability of finite binary strings. Nevertheless, they share the disadvantage of depending on the choice of the universal computer which is used as a reference computer to count the program lengths. In this paper, we propose an approach to algorithmic probability that tries to eliminate this machi...

In any organization, a primary goal of the designer is to maximize the net productive output. Every organization has a network (either hierarchical or other) that is used to divide the tasks. It is observed that the individuals in these networks trade off between their productive and managing efforts to do these tasks and the trade-off is caused by their positions and share of rewards in the ne...

This paper presents a rewriting method for Boolean circuits that minimizes small subcircuits with exact synthesis. Individual synthesis tasks are encoded as Quantified Formulas (QBFs) capture the full flexibility implementing multi-output subcircuits. is in contrast to SAT-based resynthesis, where "don't cares" computed an individual gate, and replacements confined circuitry used exclusively by...

ENCODING STRUCTURED OUTPUT VALUES Edward Loper Martha Palmer Many of the Natural Language Processing tasks that we would like to model with machine learning techniques generate structured output values, such as trees, lists, or groupings. These structured output problems can be modeled by decomposing them into a set of simpler sub-problems, with well-defined and well-constrained interdependenci...