input: Write a function to create a machine learning algorithm in Python to categorize a dataset of records. The function should use the Support Vector Classifier (SVC) from the sklearn library. If the given dataset is empty, the function should return an empty list.
  
  assert categorize_dataset([{"label": "apple", "features": [1, 1, 0]}, {"label": "banana", "features": [0, 1, 1]}, {"label": "lemon", "features": [0, 0, 1]}, {"label": "orange", "features": [1, 0, 0]}, {"label": "pear", "features": [0, 1, 0]}])=='banana'
  assert categorize_dataset([])==[]
output: def categorize_dataset(dataset):
      if not dataset:
          return []
      from sklearn.svm import SVC
      X = [entry['features'] for entry in dataset]
      y = [entry['label'] for entry in dataset]
      clf = SVC()
      clf.fit(X, y)
      return clf.predict([[0, 1, 1]])[0]