of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high

• Class: An abstract data type that encapsulates operations and data. * A class provides a type. • Object: Run time instance of a class. * Objects have state. They maintain data in attributes. * Objects have identity. They have a unique name and/or location. * Objects have behavior defined

3 Ridge regression with diagonal prior Exercise 11.6 from book (p346). 4 Linear and ridge regression on prostate cancer data (Matlab) Consider the prostate cancer dataset discussed in [HTF01]. There are 8 continuous inputs and 1 continuous response, namely lpsa, which stands for log of prostate-specific antigen. The (standardized) data is in the file prostate.mat which contains the following variables (amongst others)

2 Ridge regression with diagonal prior

In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability

prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45 % more accurate on average, when acquired from query

tasks, had a more positive attitude toward the class, and had a stronger belief that success follows from one's effort. Students who perceived performance goals as salient tended to focus on their ability, evaluating their ability negatively and attributing failure to lack of ability. The pattern

by classified it into five class attributes: very positive, positive, neutral, negative and very negative. The classification process using Naïve Bayes Classifier (NBC) with data preprocessing using tokenization, cleansing and filtering. The data used in this research are in Indonesian tweets about candidates

Previous methods for extracting attributes (e.g., capital, population) of classes (Em-pires) from Web documents or search queries assume that relevant attributes oc-cur verbatim in the source text. The ex-tracted attributes are short phrases that correspond to quantifiable properties of various

static, global, incremental, supervised and top-down discretization algorithm based on Class-Attribute Contingency Coefficient. Empirical evaluation of seven discretization algorithms on 13 real datasets and four artificial datasets showed that the proposed algorithm could generate a better