Multi-class Pattern Classification Based on a Probabilistic Model of Combining Binary Classifiers

2005 ◽  
pp. 337-342
Author(s):  
Naoto Yukinawa ◽  
Shigeyuki Oba ◽  
Kikuya Kato ◽  
Shin Ishii
Keyword(s):  
Pattern Classification ◽  
Probabilistic Model ◽  
Binary Classifiers

A Probabilistic Model for Diagnosing Misconceptions By The Pattern Classification Approach

1985 ◽  
Vol 10 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Kikumi K. Tatsuoka
Keyword(s):  
Artificial Intelligence ◽  
Item Response Theory ◽  
Item Response ◽  
Pattern Classification ◽  
Probabilistic Model ◽  
Probabilistic Approach ◽  
Response Patterns ◽  
Classification Approach ◽  
Response Errors

This paper introduces a probabilistic approach to the classification and diagnosis of erroneous rules of operations that result from misconceptions (“bugs”) in a procedural domain of arithmetic. The model is different from the usual deterministic strategies common in the field of artificial intelligence because variability of response errors is explicitly treated through item response theory. As a concrete example, we analyze a dataset that reflects the use of erroneous rules of operation in problems of signed-number subtraction. The same approach, however, is applicable to the classification of several different groups of response patterns caused by a variety of different underlying misconceptions, different backgrounds of knowledge, or treatment.

PROJECT CellNet: EVOLVING AN AUTONOMOUS PATTERN RECOGNIZER

2004 ◽  
Vol 18 (06) ◽  
pp. 1039-1056 ◽  
Author(s):  
N. KHARMA ◽  
T. KOWALIW ◽  
E. CLEMENT ◽  
C. JENSEN ◽  
A. YOUSSEF ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Pattern Classification ◽  
Black Box ◽  
Global Changes ◽  
Software System ◽  
Human Intervention ◽  
Evolutionary System ◽  
Training Set ◽  
Binary Classifiers ◽  
Standard Genetic Algorithm

We describe the desire for a black box approach to pattern classification: a generic Autonomous Pattern Recognizer, which is capable of self-adapting to specific alphabets without human intervention. The CellNet software system is introduced, an evolutionary system that optimizes a set of pattern-recognizing agents relative to a provided set of features and a given pattern database. CellNet utilizes a new genetic operator designed to facilitate a canalization of development: Merger. CellNet utilizes our own set of arbitrarily chosen features, and is applied to the CEDAR Database of handwritten Latin characters, as well as to a database of handwritten Indian digits provided by CENPARMI. CellNet's cooperative co-evolutionary approach shows significant improvement over a more standard Genetic Algorithm, both in terms of efficiency and in nearly eliminating over-fitting (to the training set). Additionally, the binary classifiers autonomously evolved by CellNet return validation accuracies approaching 98% for both Latin and Indian digits, with no global changes to the system between the two trials.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Sign in / Sign up

Export Citation Format

Share Document