Comparing categorization models.

Comparing Categorization Models | A psychological experiment

Modeling Category Intuitiveness

We asked 169 participants to spontaneously categorize nine sets of items. A category structure was assumed to be more intuitive if a large number of participants consistently produced the same classification. Our results provide a rich empirical framework for examining models of unsupervised categorization—and illustrate a corresponding profound modeling challenge. We provide a preliminary exam...

Discovering Inductive Biases in Categorization through Iterated Learning

Progress in studying human categorization has typically involved comparing generalization judgments made by people to those made by models for a variety of training conditions. In this paper, we explore an alternative method for understanding human category learning—iterated learning—which can directly expose the inductive biases of human learners and categorization models. Using a variety of s...

A Systematic Review of Banking Business Models with an Approach to Sustainable Development

Modern banks have shifted their function as purely administrative, economic and industrial entities into socio-political institutions that must be sensitive to the surrounding environment. This function has always been neglected. This study was conducted based on primary, secondary, and tertiary data and reviews the full text of 75 studies selected from more than 245 studies. The selected elect...

Large margin multinomial mixture model for text categorization

In this paper, we present a novel discriminative training method for multinomial mixture models (MMM) in text categorization based on the principle of large margin. Under some approximation and relaxation conditions, large margin estimation (LME) of MMMs can be formulated as linear programming (LP) problems, which can be efficiently and reliably solved by many general optimization tools even fo...

Concept Learning and Categorization from the Web

In previous work, we found that a great deal of information about noun attributes can be extracted from the Web using simple text patterns, and that enriching vector-based models of concepts with this information about attributes led to drastic improvements in noun categorization. We extend this previous work in two ways: (i) by comparing concept descriptions extracted using patterns with descr...