input: Write a function to create a machine learning algorithm in Python using a Support Vector Classifier (SVC) from scikit-learn to categorize a dataset of records based on their features. The function should take a dataset and a feature vector to predict, and return the predicted label. If the 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]}, 
  ], [1, 0, 1]) == ['banana']
  
  assert categorize_dataset([], [1, 0, 1]) == []
output: def categorize_dataset(dataset, feature_vector):
      if not dataset:
          return []
      X = [entry["features"] for entry in dataset]
      y = [entry["label"] for entry in dataset]
      clf = SVC()
      clf.fit(X, y)
      predicted_label = clf.predict([feature_vector])
      return predicted_label