Probabilistic finite-state machines--part II.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition or in fields to which pattern recognition is linked. In Part I of this paper, we surveyed these objects and studied their properties. In this Part II, we study the relations between probabilistic finite-state automata and other well-known devices that generate strings like hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Probabilistic finite-state machines--part I.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Benign/malignant classifier of soft tissue tumors using MR imaging.

Unlabelled: This article presents a pattern-recognition approach to the soft tissue tumors (STT) benign/malignant character diagnosis using magnetic resonance (MR) imaging applied to a large multicenter database.

Objective: To develop and test an automatic classifier of STT into benign or malignant by using classical MR imaging findings and epidemiological information.

Materials And Methods: A database of 430 patients (62% benign and 38% malignant) from several European multicenter registers.