The Problem: Semantic Gap
An image is just a big grid of numbers between [0, 245]:
컴퓨터는 이미지를 0부터 245까지 픽셀로 본다. 따라서, 컴퓨터가 이미지 분류하기 위해서 몇 가지의 장애가 있다.
def train(images, labels):
# Machine learning
return model
def predict(model, test_images):
# Use model to predict labels
return test_labels
Instead of copying label from nearest neighbor, take majority vote from K closest points
In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training.
These are hyperparameters: choices about the algorithm that we set rather than learn.
In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector.