Into the pit "machine learning combat":
The first book is a machine learning algorithm k- nearest neighbor (kNN), its working principle is: There is a sample data set, also known as the training sample set, and each data sample set are labels exist, that we know each sample set correspondence relationship between the classification of the data. After entering the new data without labels, each new data sample set and the feature data corresponding features are compared, and then concentrated feature extraction algorithm is most similar to the data sample (nearest neighbor) class label. In general, we select only the k most similar data before the sample data set, which is k- nearest neighbor in the origin of k, k is usually not an integer greater than 20. Finally, select the highest number of classified data k most similar in appearance, as the classification of new data.